Five years after CONSORT-AI, not much has changed: a call to action for artificial intelligence research in oncology
Bibliographic record
Abstract
Since its introduction in 1996, the Consolidated Standards of Reporting Trials (CONSORT) statement has been widely used to facilitate transparent reporting of randomised controlled trials (RCTs), including those in oncology.1 Due to the unique methodological and implementation challenges associated with artificial intelligence (AI)-based interventions, the CONSORT group subsequently released the CONSORT-AI extension in 2020.2 This extension introduced 14 AI-specific items to strengthen the clarity, reproducibility and reliability of trials involving AI tools.
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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 | 0.139 | 0.324 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.018 | 0.012 |
| Open science | 0.008 | 0.003 |
| Research integrity | 0.033 | 0.052 |
| Insufficient payload (model declined to judge) | 0.010 | 0.010 |
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".