The Effect of Geography on Survival of Patients with Oral Cavity Squamous Cell Carcinoma
Bibliographic record
Abstract
Objectives: Patients with oral cavity squamous cell carcinoma (OCSCC) residing in urban setting have traditionally improved survival. The main theory of the urban effect on survival is improved access to healthcare. We aimed to evaluate effects of residing in an urban setting on the survival of patients with OCSCC in a system with universal access to health care. Methods: This is a population based study set in the province of Alberta, Canada. Demographic, pathologic, treatment, and survival data was obtained from all patients diagnosed with OCSCC in the province of Alberta between 1998 and 2010. The overall and disease specific survival were calculated using Kaplan‐Meir and Cox‐Regression analysis. The log rank test was employed for comparisons between groups. Results: A total of 624 patients were included. There was no difference between patients residing in an Urban or rural setting in regards to, stage, demographics, and treatment paradigms. Urban patients benefitted from 5 and 10 year overall survival improvements (77.4%, P = 0.04) and early stage OCSCC (83.4%, P = 0.002). Conclusions: OCSSC patients residing in an urban setting have improved survival. Factors other than access to health care seem to be causing this effect.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".