Risk Assessment of Computer-Aided Diagnostic Software for Hepatic Resection
Bibliographic record
Abstract
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 ($t$) 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 cm3, if administration of bevacizumab, a post-operative therapy, is (not) adopted in the simulation,$t$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 cm3, and with (without) bevacizumab,$t$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,$t$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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".