Assessing clinician performance using a multi-modality clinical decision-support system for lung cancer prognostication
Bibliographic record
Abstract
Surgery is the primary treatment for early-stage lung cancer. Adjuvant therapy is offered to patients who are at a high risk of recurrence, however, determining the patients that would benefit from additional therapy is often difficult. In this study, we aimed to develop a clinical decision support system (CDSS) for post-surgery lung cancer prognostication integrating a multi-modality deep learning model (DLM). Pre-operative medical images and clinical, surgical, and pathological information were fed into an externally validated DLM. A CDSS was then developed to display the patient information and DLM results for potential clinical use. Four oncologists evaluated each patient's recurrence probability, their confidence level, and their post-surgery recommendations both with and without the DLM information. The CDSS DLM information demonstrated the potential to improve user prediction performance and confidence. This exploratory study is the first to integrate a multi-modality DLM for prognostication with a CDSS, as well as the first study of clinician attitudes towards CDSSs for lung cancer.
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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 | 0.005 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 0.001 |
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".