An Oral Cavity Carcinoma Nomogram to Predict Benefit of Adjuvant Radiotherapy<alt-title>Oral Cavity Adjuvant Radiotherapy Prediction Model</alt-title>
Bibliographic record
Abstract
IMPORTANCE: After surgical resection for oral cavity squamous cell carcinoma, adjuvant radiotherapy may be recommended for patients at higher risk for locoregional recurrence, but it can be difficult to predict whether a particular patient will benefit. OBJECTIVE: To construct a model to predict which patients with oral cavity squamous cell carcinoma would benefit from adjuvant radiotherapy. DESIGN AND SETTING: We constructed several types of survival models using a set of 979 patients with oral cavity squamous cell carcinoma. Covariates were age, sex, tobacco use, stage, grade, margins, and subsite. The best performing model was externally validated on a set of 431 patients. PARTICIPANTS: The model was based on a set of 979 patients with oral cavity squamous cell carcinoma, including 563 from Memorial Sloan Kettering Cancer Center, New York, New York, and 416 from the Hospital AC Camargo, São Paulo, Brazil. The validation set consisted of 431 patients from Princess Margaret Hospital, Toronto, Ontario, Canada. MAIN OUTCOME AND MEASURE: The primary outcome measure of interest was locoregional recurrence-free survival. RESULTS: The lognormal model showed the best performance per the Akaike information criterion. An online nomogram was built from this model that estimates locoregional failure-free survival with and without postoperative radiotherapy. CONCLUSIONS AND RELEVANCE: A web-based nomogram can be used as a decision aid for adjuvant treatment decisions for patients with oral cavity squamous cell carcinoma.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | medium |
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".