Are buccal cancers in India and Canada any different?
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: To compare the treatment outcomes of squamous cell carcinoma (SCC) of buccal mucosa in India and Canada. METHODS: We compared the outcome of 169 patients with SCC of buccal mucosa treated at Tata Memorial Hospital (TMH), India with 64 matched patients from Cancer Care Manitoba (CCMB), Canada. Overall and cause specific survivals for the two geographical groups were calculated by Kaplan-Meir method and compared using log rank test. Cox regression analysis was used to see impact of independent variables. RESULTS: At 5 years, CCMB patients had lower over all survival (57.4% vs. 67.1%; P = 0.002) than TMH ones but similar cause specific survival (76.4% vs. 74.2%; P = 0.690). Age had an independent influence on both over all and cause specific survival. After adjusting for the age confounding in the Cox proportional hazard model there was no difference in the overall survival of the two groups (HR = 0.84; 95% CI = 0.51, 1.40; P = 0.509). Radiated patients had three times higher risk of dying of disease than surgically treated ones (HR = 3.03; 95% CI = 1.64, 5.60; P < 0.001). CONCLUSIONS: There was no difference in the cause specific survival between the two groups. Apparent difference in the overall survival was due to the difference in the age of presentation.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".