STUDY PROTOCOL Open Access Canadian Optically-guided approach for Oral Lesions Surgical (COOLS) trial: study protocol for a randomized controlled trial
Bibliographic record
Abstract
Background: Oral cancer is a major health problem worldwide. The 5-year survival rate ranges from 30-60%, and has remained unchanged in the past few decades. This is mainly due to late diagnosis and high recurrence of the disease. Of the patients who receive treatment, up to one third suffer from a recurrence or a second primary tumor. It is apparent that one major cause of disease recurrence is clinically unrecognized field changes which extend beyond the visible tumor boundary. We have previously developed an approach using fluorescence visualization (FV) technology to improve the recognition of the field at risk surrounding a visible oral cancer that needs to be removed and preliminary results have shown a significant reduction in recurrence rates. Method/Design: This paper describes the study design of a randomized, multi-centre, double blind, controlled surgical trial, the COOLS trial. Nine institutions across Canada will recruit a total of 400 patients with oral severe dysplasia or carcinoma in situ (N = 160) and invasive squamous cell carcinoma (N = 240). Patients will be stratified by participating institution and histology grade and randomized equally into FV-guided surgery (experimental arm) or white light-guided surgery (control arm). The primary endpoint is a composite of recurrence at or 1 cm within the previous surgery site with 1) the same or higher grade histology compared to the initial diagnosis (i.e., the diagnosis used for randomization); or 2) further treatment due to the presence of severe dysplasia or higher
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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 | 0.018 | 0.023 |
| Meta-epidemiology (narrow) | 0.007 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.094 | 0.013 |
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".