Evaluation of a Novel Prognostic System for Surgically Resected Oral Cavity Carcinoma
Bibliographic record
Abstract
BACKGROUND: The Oral Cavity Predictive Score (OCCPS) is a point-based system including T/N stage, LVSI, and grade that has been validated for predicting distant metastasis. We tested its ability to predict overall survival (OS) in surgically managed oral cavity carcinoma (OCC). METHODS: Patients diagnosed 2010-2020 and treated with surgery were selected from the National Cancer Data Base (NCDB) and stratified into standard, intermediate, and high-risk categories using OCCPS. The Kaplan-Meier method compared OS in the whole cohort and select subgroups. RESULTS: Of 32 317 patients (median follow-up 38.4 months), 51.7%, 22.5%, and 25.8% were classified into standard, intermediate, and high-risk groups, respectively. Five-year OS was 73.4%, 58.6%, and 39.3% (log rank p < 0.001). The OCCPS stratified patients with negative margins and absent ENE, and stage IVA-B disease (log rank p < 0.001). CONCLUSION: In patients with OCC managed with at least definitive surgery, the OCCPS successfully stratifies patients into standard, intermediate, and high-risk groups for prediction of OS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".