Time to treatment of esophageal cancer in Ontario: A population-level cross-sectional study
Bibliographic record
Abstract
Objective Timely cancer treatment improves survival and anxiety for some sites. Patients with esophageal cancer require specific workup before treatment, which can prolong the time from diagnosis to treatment (treatment interval [TI]). The geographical variation of this interval remains uninvestigated in patients with esophageal cancer. Methods This retrospective population-level study conducted in Ontario used linked administrative health care databases. Patients treated for esophageal cancer between 2013 and 2018 were included. The TI was time from diagnosis to treatment. Patients were assigned a geographical Local Health Integration Network on the basis of postal code. Covariates included patient, disease, and diagnosing physician characteristics. Quantile regression modeled TI length at the 50th and 90th percentile and identified associated factors. Results Of 7509 patients, 78% were male and most were aged between 60 and 69 years. The 50th and 90th percentile TI was 36 (interquartile range, 22-55) and 77 days, respectively. The difference between the Local Health Integration Network with the longest and shortest TI at the 50th and 90th percentile was 18 and 25 days, respectively. Older age ( P < .0001), greater comorbidity ( P = .0005), greater material deprivation ( P = .001), rurality ( P = .03), histology ( P = .02), and treatment group ( P < .0001) were associated with a longer median TI. Older age ( P = .03), greater comorbidity ( P = .003), greater material deprivation ( P = .005), rurality ( P = .04), and treatment group ( P < .0001) were associated with a longer 90th percentile TI. Conclusions Geographic variability of time to treatment exists across Ontario. Investigation of facility-level differences is warranted. Patient and disease factors are associated with longer wait times. These results might inform future health care policy and resource allocation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".