Semi-Supervised Learning from Small Annotated Data and Large Unlabeled Data for Fine-grained PICO Entity Recognition | Paper Citation Record

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This paper cites Evidence-based medicine.

Semi-Supervised Learning from Small Annotated Data and Large Unlabeled Data for Fine-grained PICO Entity Recognition Evidence-based medicine

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This paper cites Perspective and future of evidence-based medicine.

Semi-Supervised Learning from Small Annotated Data and Large Unlabeled Data for Fine-grained PICO Entity Recognition Perspective and future of evidence-based medicine

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This paper cites Principles of evidence based medicine.

Semi-Supervised Learning from Small Annotated Data and Large Unlabeled Data for Fine-grained PICO Entity Recognition Principles of evidence based medicine

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This paper cites Ai-generated text may have a role in evidence-based medicine.

Semi-Supervised Learning from Small Annotated Data and Large Unlabeled Data for Fine-grained PICO Entity Recognition Ai-generated text may have a role in evidence-based medicine

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This paper cites Leveraging generative ai for clinical evidence synthesis needs to ensure trustworthiness.

Semi-Supervised Learning from Small Annotated Data and Large Unlabeled Data for Fine-grained PICO Entity Recognition Leveraging generative ai for clinical evidence synthesis needs to ensure trustworthiness

Reference 5

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This paper cites Meta-analysis as evidence: building a better pyramid.

Semi-Supervised Learning from Small Annotated Data and Large Unlabeled Data for Fine-grained PICO Entity Recognition Meta-analysis as evidence: building a better pyramid

Reference 6

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This paper cites Systematic reviews: synthesis of best evidence for clinical decisions.

Semi-Supervised Learning from Small Annotated Data and Large Unlabeled Data for Fine-grained PICO Entity Recognition Systematic reviews: synthesis of best evidence for clinical decisions

Reference 7

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This paper cites Seventy-five trials and eleven systematic reviews a day: how will we ever keep up? PLoS medicine, 7(9):e1000326, 2010.

Semi-Supervised Learning from Small Annotated Data and Large Unlabeled Data for Fine-grained PICO Entity Recognition Seventy-five trials and eleven systematic reviews a day: how will we ever keep up? PLoS medicine, 7(9):e1000326, 2010

Reference 8

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This paper cites Analysis of the time and workers needed to conduct systematic reviews of medical interventions using data from the prospero registry.

Semi-Supervised Learning from Small Annotated Data and Large Unlabeled Data for Fine-grained PICO Entity Recognition Analysis of the time and workers needed to conduct systematic reviews of medical interventions using data from the prospero registry

Reference 9

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Semi-Supervised Learning from Small Annotated Data and Large Unlabeled Data for Fine-grained PICO Entity Recognition Unresolved cited work

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This paper cites Answering clinical questions with knowledge-based and sta- tistical techniques.

Semi-Supervised Learning from Small Annotated Data and Large Unlabeled Data for Fine-grained PICO Entity Recognition Answering clinical questions with knowledge-based and sta- tistical techniques

Reference 11

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This paper cites Combination of conditional random field with a rule based method in the extraction of pico elements.

Semi-Supervised Learning from Small Annotated Data and Large Unlabeled Data for Fine-grained PICO Entity Recognition Combination of conditional random field with a rule based method in the extraction of pico elements

Reference 12

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This paper cites Pico element detection in medical text via long short-term memory neural networks.

Semi-Supervised Learning from Small Annotated Data and Large Unlabeled Data for Fine-grained PICO Entity Recognition Pico element detection in medical text via long short-term memory neural networks

Reference 13

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This paper cites Framewise phoneme classification with bidirectional lstm and other neural network architectures.

Semi-Supervised Learning from Small Annotated Data and Large Unlabeled Data for Fine-grained PICO Entity Recognition Framewise phoneme classification with bidirectional lstm and other neural network architectures

Reference 14

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This paper cites End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF.

Semi-Supervised Learning from Small Annotated Data and Large Unlabeled Data for Fine-grained PICO Entity Recognition End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF

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This paper cites Advancing pico element detection in biomedical text via deep neural networks.

Semi-Supervised Learning from Small Annotated Data and Large Unlabeled Data for Fine-grained PICO Entity Recognition Advancing pico element detection in biomedical text via deep neural networks

Reference 16

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This paper cites Improving reference prioriti- sation with pico recognition.

Semi-Supervised Learning from Small Annotated Data and Large Unlabeled Data for Fine-grained PICO Entity Recognition Improving reference prioriti- sation with pico recognition

Reference 17

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This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Semi-Supervised Learning from Small Annotated Data and Large Unlabeled Data for Fine-grained PICO Entity Recognition BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

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Semi-Supervised Learning from Small Annotated Data and Large Unlabeled Data for Fine-grained PICO Entity Recognition Biobert: a pre-trained biomedical language representation model for biomedical text mining

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Semi-Supervised Learning from Small Annotated Data and Large Unlabeled Data for Fine-grained PICO Entity Recognition RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 20

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Semi-Supervised Learning from Small Annotated Data and Large Unlabeled Data for Fine-grained PICO Entity Recognition SciBERT: A Pretrained Language Model for Scientific Text

Reference 21

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This paper cites A corpus with multi-level annotations of patients, interventions and outcomes to sup- port language processing for medical literature.

Semi-Supervised Learning from Small Annotated Data and Large Unlabeled Data for Fine-grained PICO Entity Recognition A corpus with multi-level annotations of patients, interventions and outcomes to sup- port language processing for medical literature

Reference 22

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This paper cites Towards precise pico ex- traction from abstracts of randomized controlled trials using a section-specific learning approach.

Semi-Supervised Learning from Small Annotated Data and Large Unlabeled Data for Fine-grained PICO Entity Recognition Towards precise pico ex- traction from abstracts of randomized controlled trials using a section-specific learning approach

Reference 23

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This paper cites A study on agreement in pico span annotations.

Semi-Supervised Learning from Small Annotated Data and Large Unlabeled Data for Fine-grained PICO Entity Recognition A study on agreement in pico span annotations

Reference 24

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This paper cites Correcting crowd- sourced annotations to improve detection of outcome types in evidence based medicine.

Semi-Supervised Learning from Small Annotated Data and Large Unlabeled Data for Fine-grained PICO Entity Recognition Correcting crowd- sourced annotations to improve detection of outcome types in evidence based medicine

Reference 25

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This paper cites Not so weak pico: leveraging weak supervision for participants, interventions, and outcomes recognition for systematic review automation.

Semi-Supervised Learning from Small Annotated Data and Large Unlabeled Data for Fine-grained PICO Entity Recognition Not so weak pico: leveraging weak supervision for participants, interventions, and outcomes recognition for systematic review automation

Reference 26

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This paper cites An annotated corpus of clinical trial publications supporting schema-based relational information extraction.

Semi-Supervised Learning from Small Annotated Data and Large Unlabeled Data for Fine-grained PICO Entity Recognition An annotated corpus of clinical trial publications supporting schema-based relational information extraction

Reference 27

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Semi-Supervised Learning from Small Annotated Data and Large Unlabeled Data for Fine-grained PICO Entity Recognition Introduction to systematic review and meta-analysis

Reference 28

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Semi-Supervised Learning from Small Annotated Data and Large Unlabeled Data for Fine-grained PICO Entity Recognition Pico cor- pus: a publicly available corpus to support automatic data extraction from biomedical literature

Reference 29

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Semi-Supervised Learning from Small Annotated Data and Large Unlabeled Data for Fine-grained PICO Entity Recognition Automatic text classification to support systematic reviews in medicine

Reference 30

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Semi-Supervised Learning from Small Annotated Data and Large Unlabeled Data for Fine-grained PICO Entity Recognition A survey on deep semi-supervised learning

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Semi-Supervised Learning from Small Annotated Data and Large Unlabeled Data for Fine-grained PICO Entity Recognition Prototype-guided pseudo labeling for semi-supervised text classification

Reference 32

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Semi-Supervised Learning from Small Annotated Data and Large Unlabeled Data for Fine-grained PICO Entity Recognition Decoupled deep neural network for semi- supervised semantic segmentation

Reference 33

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Semi-Supervised Learning from Small Annotated Data and Large Unlabeled Data for Fine-grained PICO Entity Recognition Semi-supervised pca-based face recognition using self-training

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Semi-Supervised Learning from Small Annotated Data and Large Unlabeled Data for Fine-grained PICO Entity Recognition Ecml-pkdd discovery challenge 2006 overview

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Semi-Supervised Learning from Small Annotated Data and Large Unlabeled Data for Fine-grained PICO Entity Recognition Semi-supervised document retrieval

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Semi-Supervised Learning from Small Annotated Data and Large Unlabeled Data for Fine-grained PICO Entity Recognition Semi-supervised ranking for document retrieval

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Semi-Supervised Learning from Small Annotated Data and Large Unlabeled Data for Fine-grained PICO Entity Recognition Semi-supervised classification for extracting protein interaction sentences using dependency parsing

Reference 38

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Semi-Supervised Learning from Small Annotated Data and Large Unlabeled Data for Fine-grained PICO Entity Recognition Domain-specific language model pretraining for biomedical natural language processing

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Semi-Supervised Learning from Small Annotated Data and Large Unlabeled Data for Fine-grained PICO Entity Recognition Using pseudo-labeling to improve performance of deep neural networks for animal identification

Reference 40

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Semi-Supervised Learning from Small Annotated Data and Large Unlabeled Data for Fine-grained PICO Entity Recognition Unsupervised data augmentation for consistency training

Reference 41

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Semi-Supervised Learning from Small Annotated Data and Large Unlabeled Data for Fine-grained PICO Entity Recognition De-biasing Distantly Supervised Named Entity Recognition via Causal Intervention

Reference 42

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This paper cites Improving large language models for clinical named entity recognition via prompt engineering.

Semi-Supervised Learning from Small Annotated Data and Large Unlabeled Data for Fine-grained PICO Entity Recognition Improving large language models for clinical named entity recognition via prompt engineering

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This paper cites A span-based model for extracting overlapping pico entities from randomized controlled trial publications.

Semi-Supervised Learning from Small Annotated Data and Large Unlabeled Data for Fine-grained PICO Entity Recognition A span-based model for extracting overlapping pico entities from randomized controlled trial publications

Reference 44

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This paper cites Auto- matic data extraction to support meta-analysis statistical analysis: a case study on breast cancer.

Semi-Supervised Learning from Small Annotated Data and Large Unlabeled Data for Fine-grained PICO Entity Recognition Auto- matic data extraction to support meta-analysis statistical analysis: a case study on breast cancer

Reference 45

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Semi-Supervised Learning from Small Annotated Data and Large Unlabeled Data for Fine-grained PICO Entity Recognition Natural language processing with Python: analyzing text with the natural language toolkit

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This paper cites Introduction to the CoNLL-2000 Shared Task: Chunking.

Semi-Supervised Learning from Small Annotated Data and Large Unlabeled Data for Fine-grained PICO Entity Recognition Introduction to the CoNLL-2000 Shared Task: Chunking

Reference 47

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This paper cites Document information extraction via global tagging.

Semi-Supervised Learning from Small Annotated Data and Large Unlabeled Data for Fine-grained PICO Entity Recognition Document information extraction via global tagging

Reference 48

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This paper cites Optimized glycemic control of type 2 diabetes with reinforcement learning: a proof-of-concept trial.

Semi-Supervised Learning from Small Annotated Data and Large Unlabeled Data for Fine-grained PICO Entity Recognition Optimized glycemic control of type 2 diabetes with reinforcement learning: a proof-of-concept trial

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:45:00.982861Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0eb45c09-651d-47a2-a8f7-bcfd72e1093f · outbound

This paper cites Subgroup analysis and other (mis) uses of baseline data in clinical trials.

Semi-Supervised Learning from Small Annotated Data and Large Unlabeled Data for Fine-grained PICO Entity Recognition Subgroup analysis and other (mis) uses of baseline data in clinical trials

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:45:00.949413Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation fd3ddcad-544f-4c09-89dc-f660a90ef63f · outbound

This paper cites Misuse of baseline comparison tests and subgroup analyses in surgical trials.

Semi-Supervised Learning from Small Annotated Data and Large Unlabeled Data for Fine-grained PICO Entity Recognition Misuse of baseline comparison tests and subgroup analyses in surgical trials

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:45:00.923914Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 809faa79-9341-43a1-84b8-78dece6460ac · outbound

This paper cites seqeval: A python framework for sequence labeling evaluation.

Semi-Supervised Learning from Small Annotated Data and Large Unlabeled Data for Fine-grained PICO Entity Recognition seqeval: A python framework for sequence labeling evaluation

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:45:00.900834Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation dbf306bc-2b4f-4ed5-b19f-9d7f2c98284c · outbound

This paper cites The automatic detection of dataset names in scientific articles.

Semi-Supervised Learning from Small Annotated Data and Large Unlabeled Data for Fine-grained PICO Entity Recognition The automatic detection of dataset names in scientific articles

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:45:00.881152Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8a3fa2f5-c6d1-4d91-a9ef-063b517fe026 · outbound

This paper cites A probabilistic model for identifying protein names and their name boundaries.

Semi-Supervised Learning from Small Annotated Data and Large Unlabeled Data for Fine-grained PICO Entity Recognition A probabilistic model for identifying protein names and their name boundaries

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:45:00.860110Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7a81255d-9dd9-4625-8c11-084d356ce240 · outbound

This paper cites Knowledge distillation for low-power object detection: A simple tech- nique and its extensions for training compact models using unlabeled data.

Semi-Supervised Learning from Small Annotated Data and Large Unlabeled Data for Fine-grained PICO Entity Recognition Knowledge distillation for low-power object detection: A simple tech- nique and its extensions for training compact models using unlabeled data

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:45:00.840800Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f91066f0-e8d9-4ec4-b7ca-a9583168b1ec · outbound

This paper cites Boosting semi-supervised learning by exploiting all unlabeled data.

Semi-Supervised Learning from Small Annotated Data and Large Unlabeled Data for Fine-grained PICO Entity Recognition Boosting semi-supervised learning by exploiting all unlabeled data

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:45:00.822084Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 51894494-392e-4901-81f9-b5d8571d448f · outbound

This paper cites Realistic evaluation of deep semi-supervised learning algorithms.

Semi-Supervised Learning from Small Annotated Data and Large Unlabeled Data for Fine-grained PICO Entity Recognition Realistic evaluation of deep semi-supervised learning algorithms

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:45:00.801235Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 55de9b02-c3bf-4b62-92d1-507beba56628 · outbound

This paper cites Unlabeled data: Now it helps, now it doesn’t.

Semi-Supervised Learning from Small Annotated Data and Large Unlabeled Data for Fine-grained PICO Entity Recognition Unlabeled data: Now it helps, now it doesn’t

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:45:00.781251Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation fea74b9c-aabc-4cff-b738-e284108eeecc · outbound

This paper cites Towards more generalizable and accurate sentence classification in medical abstracts with less data.

Semi-Supervised Learning from Small Annotated Data and Large Unlabeled Data for Fine-grained PICO Entity Recognition Towards more generalizable and accurate sentence classification in medical abstracts with less data

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:45:00.762984Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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