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Paper Citation Record · LEDGER

Value-Monotonicity Matters: A Concordance Loss for Deep Survival Prediction

As of 10 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 2 inbound Pith citation observations for arXiv:2607.16802.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2607.16802 v1

Coverage vector

measured 46 of 46 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T19:58:27.952103Z

measured 48 of 48 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T04:26:53.282393Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-05T04:26:53.357951Z

Reference resolution

46 of 46 outbound references displayed

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Outbound references

Observation f5676368-58f0-4a1a-ba51-8d87fbd52542 · outbound

This paper cites Harrell, Robert M.

Value-Monotonicity Matters: A Concordance Loss for Deep Survival Prediction Harrell, Robert M

Reference 1

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Observation 07c3bbcc-665b-48b1-9192-6cc29bca4665 · outbound

This paper cites Pencina, Ralph B.

Value-Monotonicity Matters: A Concordance Loss for Deep Survival Prediction Pencina, Ralph B

Reference 2

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Observation b3be0dd1-c1d0-40d3-b549-8106af384c09 · outbound

This paper cites Katzman, Uri Shaham, Alexander Cloninger, Jonathan Bates, Tingting Jiang, and Yuval Kluger.

Value-Monotonicity Matters: A Concordance Loss for Deep Survival Prediction Katzman, Uri Shaham, Alexander Cloninger, Jonathan Bates, Tingting Jiang, and Yuval Kluger

Reference 3

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Observation 371e38c8-24c0-442e-b308-c127700d4644 · outbound

This paper cites an unresolved cited work.

Value-Monotonicity Matters: A Concordance Loss for Deep Survival Prediction Unresolved cited work

Reference 4

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Observation fc345d2d-6d22-4699-94c1-0ab591cd67b2 · outbound

This paper cites Zame, Jinsung Yoon, and Mihaela van der Schaar.

Value-Monotonicity Matters: A Concordance Loss for Deep Survival Prediction Zame, Jinsung Yoon, and Mihaela van der Schaar

Reference 5

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Observation 491a4b40-d625-4e63-a866-c3f858784e5c · outbound

This paper cites Learning patient-specific cancer survival distributions as a sequence of dependent regressors.

Value-Monotonicity Matters: A Concordance Loss for Deep Survival Prediction Learning patient-specific cancer survival distributions as a sequence of dependent regressors

Reference 6

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Observation 12a5065d-692d-4811-bed2-9de9d743d6bb · outbound

This paper cites Gensheimer and Balasubramanian Narasimhan.

Value-Monotonicity Matters: A Concordance Loss for Deep Survival Prediction Gensheimer and Balasubramanian Narasimhan

Reference 7

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Observation 8dbb4486-bb41-497d-8761-8500ac27a77f · outbound

This paper cites Time-to-event prediction with neural networks and Cox regression.Journal of Machine Learning Research, 20(129):1–30, 2019.

Value-Monotonicity Matters: A Concordance Loss for Deep Survival Prediction Time-to-event prediction with neural networks and Cox regression.Journal of Machine Learning Research, 20(129):1–30, 2019

Reference 8

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Observation e2dd68d0-8b14-4cae-8617-a60c280e65d1 · outbound

This paper cites SODEN: A scalable continuous-time survival model through ordinary differential equation networks.Journal of Machine Learning Research, 23 (34):1–29, 2022.

Value-Monotonicity Matters: A Concordance Loss for Deep Survival Prediction SODEN: A scalable continuous-time survival model through ordinary differential equation networks.Journal of Machine Learning Research, 23 (34):1–29, 2022

Reference 9

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Observation bc5e3170-f660-4a50-b518-fa012c80d91b · outbound

This paper cites an unresolved cited work.

Value-Monotonicity Matters: A Concordance Loss for Deep Survival Prediction Unresolved cited work

Reference 10

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Observation 8c49a3a3-d896-4aee-ab59-80be0554f819 · outbound

This paper cites Chen, Ming Y.

Value-Monotonicity Matters: A Concordance Loss for Deep Survival Prediction Chen, Ming Y

Reference 11

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Observation 5fedbee8-9ae3-4b68-8469-1b250c6fc50a · outbound

This paper cites Chen, Ming Y.

Value-Monotonicity Matters: A Concordance Loss for Deep Survival Prediction Chen, Ming Y

Reference 12

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Observation d2f5bc4a-16a8-4e66-a698-494da5e93684 · outbound

This paper cites Chen, Drew F.

Value-Monotonicity Matters: A Concordance Loss for Deep Survival Prediction Chen, Drew F

Reference 13

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Observation 4eacf41f-8924-42d8-aeda-ba6a2d5e120a · outbound

This paper cites TripleSurv: Triplet time-adaptive coordinate learning approach for survival analysis.IEEE Transactions on Knowledge and Data Engineering, 36(12):9464–9475, 2024.

Value-Monotonicity Matters: A Concordance Loss for Deep Survival Prediction TripleSurv: Triplet time-adaptive coordinate learning approach for survival analysis.IEEE Transactions on Knowledge and Data Engineering, 36(12):9464–9475, 2024

Reference 14

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Observation 4f78a14a-5447-45c1-9586-35980396455a · outbound

This paper cites Chen, Tong Ding, Ming Y.

Value-Monotonicity Matters: A Concordance Loss for Deep Survival Prediction Chen, Tong Ding, Ming Y

Reference 15

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Observation 9efa5c7f-4703-41cd-aa1c-10e60639e164 · outbound

This paper cites Lu, Bowen Chen, Drew F.

Value-Monotonicity Matters: A Concordance Loss for Deep Survival Prediction Lu, Bowen Chen, Drew F

Reference 16

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Observation 76314461-29e7-4c9b-8d4a-f8622272a9b7 · outbound

This paper cites Multimodal Whole Slide Foundation Model for Pathology.

Value-Monotonicity Matters: A Concordance Loss for Deep Survival Prediction Multimodal Whole Slide Foundation Model for Pathology

Reference 17

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Observation 5657ae6f-ff15-47fc-95de-0e6b1519aae7 · outbound

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Value-Monotonicity Matters: A Concordance Loss for Deep Survival Prediction Unresolved cited work

Reference 18

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Observation 48a4d523-93c7-44a2-92ae-6c432fbea350 · outbound

This paper cites On the consistency of AUC pairwise optimization.

Value-Monotonicity Matters: A Concordance Loss for Deep Survival Prediction On the consistency of AUC pairwise optimization

Reference 19

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Observation 9b401a0c-1c34-4e52-a51d-f27d8f6bc4aa · outbound

This paper cites A statistical learning take on the concordance index for survival analysis.

Value-Monotonicity Matters: A Concordance Loss for Deep Survival Prediction A statistical learning take on the concordance index for survival analysis

Reference 20

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Observation 260f2919-7f26-4b92-a1f1-b16cf6e24a6b · outbound

This paper cites Bartlett, Michael I.

Value-Monotonicity Matters: A Concordance Loss for Deep Survival Prediction Bartlett, Michael I

Reference 21

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Observation ce987983-6dd1-47f5-ba9b-223b5d2fe018 · outbound

This paper cites How to compare different loss functions and their risks.Constructive Approximation, 26(2):225–287, 2007.

Value-Monotonicity Matters: A Concordance Loss for Deep Survival Prediction How to compare different loss functions and their risks.Constructive Approximation, 26(2):225–287, 2007

Reference 22

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Observation d15e7e9d-9ac3-45d0-b1d7-b59c23f451fb · outbound

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Value-Monotonicity Matters: A Concordance Loss for Deep Survival Prediction Unresolved cited work

Reference 23

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Observation ce18b511-fbd5-4ce4-bbbb-bf7ab6efbcf9 · outbound

This paper cites A general approximation framework for direct optimization of information retrieval measures.Information Retrieval, 13(4):375–397, 2010.

Value-Monotonicity Matters: A Concordance Loss for Deep Survival Prediction A general approximation framework for direct optimization of information retrieval measures.Information Retrieval, 13(4):375–397, 2010

Reference 24

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Observation cff70ae8-b5f8-493b-8ca9-ab8282feb4f0 · outbound

This paper cites A multimodal and multi-centric head and neck cancer dataset for segmentation, diagnosis and outcome prediction.

Value-Monotonicity Matters: A Concordance Loss for Deep Survival Prediction A multimodal and multi-centric head and neck cancer dataset for segmentation, diagnosis and outcome prediction

Reference 25

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Observation ee6cf63e-89e3-4fc0-8d94-75349e30a15e · outbound

This paper cites HEad and neCK TumOR (HECKTOR) 2025: Benchmark of Segmentation, Diagnosis, and Prognosis in Multimodal PET/CT.

Value-Monotonicity Matters: A Concordance Loss for Deep Survival Prediction HEad and neCK TumOR (HECKTOR) 2025: Benchmark of Segmentation, Diagnosis, and Prognosis in Multimodal PET/CT

Reference 26

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Observation d590eb7d-48a8-4a32-b28b-6ab3e371e120 · outbound

This paper cites Villanueva-Meyer, Jeffrey D.

Value-Monotonicity Matters: A Concordance Loss for Deep Survival Prediction Villanueva-Meyer, Jeffrey D

Reference 27

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This paper cites The university of pennsylvania glioblastoma (UPenn-GBM) cohort: advanced MRI, clinical, genomics, & radiomics.Scientific Data, 9(1):453, 2022.

Value-Monotonicity Matters: A Concordance Loss for Deep Survival Prediction The university of pennsylvania glioblastoma (UPenn-GBM) cohort: advanced MRI, clinical, genomics, & radiomics.Scientific Data, 9(1):453, 2022

Reference 28

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Observation 5e394dc7-cce1-4ce7-b8b2-24ee08681370 · outbound

This paper cites Shah, Suet-Feung Chin, Gulisa Turashvili, Oscar M.

Value-Monotonicity Matters: A Concordance Loss for Deep Survival Prediction Shah, Suet-Feung Chin, Gulisa Turashvili, Oscar M

Reference 29

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Observation 4223cb78-c8d4-4bab-badf-97f976abab74 · outbound

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Value-Monotonicity Matters: A Concordance Loss for Deep Survival Prediction Knaus, Frank E

Reference 30

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Observation 12e3d27f-d6b0-4483-a59f-0f065d4d53c3 · outbound

This paper cites Schumacher, G.

Value-Monotonicity Matters: A Concordance Loss for Deep Survival Prediction Schumacher, G

Reference 31

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Observation 81b6b14f-350e-45f1-b863-460cbc81bc38 · outbound

This paper cites Katzmann, Robert A.

Value-Monotonicity Matters: A Concordance Loss for Deep Survival Prediction Katzmann, Robert A

Reference 32

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Observation 1fd6247e-f311-44f0-a95d-69dd776febb2 · outbound

This paper cites Hosmer, Stanley Lemeshow, and Susanne May.Applied Survival Analysis: Regression Modeling of Time-to-Event Data.

Value-Monotonicity Matters: A Concordance Loss for Deep Survival Prediction Hosmer, Stanley Lemeshow, and Susanne May.Applied Survival Analysis: Regression Modeling of Time-to-Event Data

Reference 33

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Observation 2307fe89-ec24-4dd5-a28b-98cae770464c · outbound

This paper cites Therneau and Patricia M.

Value-Monotonicity Matters: A Concordance Loss for Deep Survival Prediction Therneau and Patricia M

Reference 34

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Observation 196c9ae2-fd56-4602-b937-1c357ec9a1ff · outbound

This paper cites Loprinzi, John A.

Value-Monotonicity Matters: A Concordance Loss for Deep Survival Prediction Loprinzi, John A

Reference 35

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This paper cites Surveillance, epidemiology, and end results (SEER) program.

Value-Monotonicity Matters: A Concordance Loss for Deep Survival Prediction Surveillance, epidemiology, and end results (SEER) program

Reference 36

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Observation ce318394-99d1-4af2-b96f-ad1ab25560d0 · outbound

This paper cites Weinstein, Eric A.

Value-Monotonicity Matters: A Concordance Loss for Deep Survival Prediction Weinstein, Eric A

Reference 37

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This paper cites Simpson, Jacob Peoples, John M.

Value-Monotonicity Matters: A Concordance Loss for Deep Survival Prediction Simpson, Jacob Peoples, John M

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This paper cites The cancer imaging archive (TCIA): Maintaining and operating a public information repository.Journal of Digital Imaging, 26(6):1045–1057, 2013.

Value-Monotonicity Matters: A Concordance Loss for Deep Survival Prediction The cancer imaging archive (TCIA): Maintaining and operating a public information repository.Journal of Digital Imaging, 26(6):1045–1057, 2013

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This paper cites Kogalur, Eugene H.

Value-Monotonicity Matters: A Concordance Loss for Deep Survival Prediction Kogalur, Eugene H

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This paper cites Assessment and comparison of prognostic classification schemes for survival data.Statistics in Medicine, 18(17-18):2529–2545, 1999.

Value-Monotonicity Matters: A Concordance Loss for Deep Survival Prediction Assessment and comparison of prognostic classification schemes for survival data.Statistics in Medicine, 18(17-18):2529–2545, 1999

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This paper cites an unresolved cited work.

Value-Monotonicity Matters: A Concordance Loss for Deep Survival Prediction Unresolved cited work

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Observation 15c44d15-7b75-4f31-a186-d33f9b12f1d6 · outbound

This paper cites Bartlett and Shahar Mendelson.

Value-Monotonicity Matters: A Concordance Loss for Deep Survival Prediction Bartlett and Shahar Mendelson

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Observation 4620e480-754c-4b0f-adf2-d745c02bc53e · outbound

This paper cites Probability inequalities for sums of bounded random variables.Journal of the American Statistical Association, 58(301):13–30, 1963.

Value-Monotonicity Matters: A Concordance Loss for Deep Survival Prediction Probability inequalities for sums of bounded random variables.Journal of the American Statistical Association, 58(301):13–30, 1963

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This paper cites On the method of bounded differences.

Value-Monotonicity Matters: A Concordance Loss for Deep Survival Prediction On the method of bounded differences

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Observation 2a2e20bc-7025-4c19-895d-a7569f864370 · outbound

This paper cites Springer-Verlag, 1991.

Value-Monotonicity Matters: A Concordance Loss for Deep Survival Prediction Springer-Verlag, 1991

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Pith citing papers

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HERMES: A Hybrid Ensemble for Head-and-Neck Tumor Segmentation, TN Staging, and Recurrence-Free Survival on PET/CT cites this paper.

HERMES: A Hybrid Ensemble for Head-and-Neck Tumor Segmentation, TN Staging, and Recurrence-Free Survival on PET/CT Value-Monotonicity Matters: A Concordance Loss for Deep Survival Prediction

Reference 14

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HERMES: A Hybrid Ensemble for Head-and-Neck Tumor Segmentation, TN Staging, and Recurrence-Free Survival on PET/CT cites this paper.

HERMES: A Hybrid Ensemble for Head-and-Neck Tumor Segmentation, TN Staging, and Recurrence-Free Survival on PET/CT Value-Monotonicity Matters: A Concordance Loss for Deep Survival Prediction

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