Time to Treatment of Esophageal Cancer in Ontario: A Population-Based Study
Bibliographic record
Abstract
Background: A patient’s journey from esophageal cancer diagnosis to treatment (treatment interval) is complex. They require numerous investigations and specialist visits before treatment can begin. Surgery is the cornerstone of treatment for resectable disease. Streamlining this pathway may shorten the treatment interval length, but this has not yet been studied in a contemporary Canadian cohort of patients with esophageal cancer. We aim to describe the length and geographical variation of the treatment interval (TI) by Local Health Integrated Networks (LHINs) and time to surgery (TTS) by Thoracic Cancer Surgery Centres (TCSCs) in Ontario, and identify factors associated with longer wait times. Methods: We conducted a population-level study using health administrative databases in Ontario, Canada. We identified patients diagnosed with esophageal cancer between January 2013 and December 2018 who underwent treatment and had at least one staging investigation or specialist visit. TI length was defined as the number of days from the date of a biopsy to the first treatment. TTS length was measured in a subgroup of patients undergoing neoadjuvant chemoradiotherapy then surgery and was defined as the number of days from the date of a biopsy to the surgery date. Univariate quantile regression was used to measure lengths at the median and 90th percentile, and multivariable quantile regression was used to identify associated factors. Results: Of the 5,759 patients identified, median and 90th percentile TI length was 36 and 77 days, respectively. The difference between the LHIN with the longest and shortest TI at the 50th and 90th percentile was 18 and 25 days, respectively. Older age, higher comorbidity, higher material deprivation, rurality, and treatment group were associated with a longer TI. Of the 733 subgroup patients, median and 90th percentile TTS length was 140 and 171 days, respectively. The difference between the TCSC with the longest and shortest TTS at the 50th and 90th percentile was 31 and 22 days, respectively. Older age was associated with a longer TTS. Conclusion: There is significant geographical variation in wait times to esophageal cancer treatment across the province. We have identified vulnerable patient populations at risk for protracted wait times.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".