Development and validation of a prediction-score model for distant metastases in major salivary gland carcinoma.
Bibliographic record
Abstract
6085 Background: We developed and validated a prediction-score for distant metastases (DM) in major salivary gland carcinoma (SGC). Methods: Patients with SGC treated with curative-intent surgery +/- postoperative radiation therapy (PORT) at 4 tertiary cancer centers were divided into discovery (institution A&B) and validation (institution C&D) cohorts. Multivariable analysis using competing risk regression was used to identify predictors of DM in the discovery cohort and create a prediction score. The optimal score cut-off for high vs low-DM risk was determined using a minimal p-value approach. The results were subsequently evaluated in the validation cohort. The cumulative incidence and Kaplan-Meier methods were used to analyze DM and overall survival (OS), respectively. Results: Overall, 1035 patients were included (Table). In the discovery cohort, DM predictors (risk score coefficient) were: positive margin (0.6), pT3-4 (0.7), pN+ (0.7), lymphovascular invasion (LVI; 0.8), and high risk histology* (1.2). High DM-risk SGC was defined by sum of coefficients greater than 2. In the discovery cohort, the 5-year cumulative incidence of DM for high vs low risk SGC was 50% vs 8%; p < 0.01; these results were similar in the validation cohort (44% vs 4% at 5 years; p < 0.01). In the combined cohorts, this model predicted distant-only failure (40% vs 6%, p < 0.01) and late ( > 2yr post surgery) DM (22% vs 4%; p < 0.01). Patients with high DM-risk SGC had an increased incidence of DM in the subgroup receiving PORT (46% vs 8%; p < 0.01) or concurrent chemotherapy (71% vs 34%; p < 0.01). The 5-yr OS for high vs low risk SGC was 48% vs 92% (p < 0.01). Conclusions: This validated prediction score model may be used to identify SGC patients at increased risk for DM and select those who may benefit from prospective evaluation of treatment intensification and/or surveillance strategies. Baseline characteristics. [Table: see text]
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".