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Record W3142047004 · doi:10.1109/trpms.2021.3071148

Risk Assessment of Computer-Aided Diagnostic Software for Hepatic Resection

2021· article· en· W3142047004 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Radiation and Plasma Medical Sciences · 2021
Typearticle
Languageen
FieldHealth Professions
TopicArtificial Intelligence in Healthcare
Canadian institutionsMcGill University
FundersQatar National Research FundQatar Foundation
KeywordsComputer scienceSoftwareResectionMedicineSoftware engineeringSurgeryProgramming language

Abstract

fetched live from OpenAlex

In this article, we study the indirect relationship between the adoption of computer-aided detection or diagnostic (CADe or CADx) systems for hepatic resection (HR) and the patient’s health post-surgery. We vary the number, actual size, and the estimated size of tumors along with model parameters of tumor growth over 1000 simulations of HR according to predefined statistical distributions of parameter values. The average time ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula> ) taken by the tumors to relapse is assessed for the nonadoption of computer-aided detection or diagnostic (CAD) (case 1), the adoption of semiautomatic CAD (case 2), and the adoption of automatic CAD (case 3) in HR. In this study, we have simulated 126 automatic CAD algorithms (case 3). For tumor volumes (TV) less than 50 cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> , if administration of bevacizumab, a post-operative therapy, is (not) adopted in the simulation, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula> is found to be 646, 84, and 60 days (40, 24, and 17 days) for case 1, case 2, and case 3, respectively. For TV greater than 50 cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> , and with (without) bevacizumab, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula> is found to be 86, 1, and 6 days (28, 6, and 3 days) for case 1, case 2, and case 3, respectively. For with (without) bevacizumab treatment and for all tumor volumes, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula> is found to be 260, 90, and 104 days (38, 13, and 11 days) for case 1, case 2, and case 3, respectively. We have observed that the tumors relapsed quickly in those cases where CAD was adopted.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.940
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.103
GPT teacher head0.457
Teacher spread0.354 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it