Predictors of prolonged treatment time intervals in oral cavity cancer
Bibliographic record
Abstract
OBJECTIVES: Delays in treatment time intervals have been associated with overall survival in oral cavity squamous cell carcinoma (OCSCC). The aim of this study was to identify bottlenecks leading to prolonged treatment intervals. MATERIAL AND METHODS: A retrospective analysis was conducted using a cohort of OCSCC patients who underwent surgery and adjuvant radiation therapy. The endpoints of interest were prolonged treatment intervals. Multivariable logistic regression was used to adjust for patient and tumour characteristics. RESULTS: Median diagnosis-to-treatment interval (DTI) and surgery to initiation of postoperative radiation therapy interval (S-PORT) were 39 days (IQR 30-54) and 64 days (IQR 54-66), respectively. Prolonged DTI was associated with older age, worse Charlson Comorbidity index scores and worse T stages. Patients with prolonged DTI had longer times to preoperative imaging reports (25 vs 9 days; P < 0.01). Time to preoperative pathology did not differ. Prolonged S-PORT was associated with longer times to pathology report (28 vs 18 days; P < 0.01), to maxillofacial consult (38 vs 15 days; P < 0.01) and to maxillofacial approval of radiation (50 vs 28 days; P < 0.01). In patients requiring medical oncology consults, those with prolonged S-PORT had longer waiting times until consultation (58 vs 38 days; P = 0.02). Multivariate analysis showed independent predictors of prolonged DTI: time to preoperative imaging; and prolonged S-PORT: time to pathology report, time to maxillofacial consult, and time to medical oncology consult. CONCLUSIONS: Strategies targeting these organizational bottlenecks may be effective for shortening treatment time intervals, hence representing potential opportunities for improving oncological outcomes in OCSCC patients.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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, 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".