Using administrative databases to measure waiting times for patients undergoing major cancer surgery in Ontario, 1993-2000.
Bibliographic record
Abstract
PURPOSE: To determine how long patients in Ontario waited for major breast, colorectal, lung or prostate cancer surgery in the years 1993-2000. METHODS: "Surgical waiting time" was defined as the interval from date of preoperative surgeon consult to date of hospital admission for surgery. We created patient cohorts by linking appropriate diagnosis and procedure codes from Canadian Institutes of Health Information data. Scrambled unique surgeon identifiers were obtained from Ontario Health Insurance Plan data. Changes in median surgical waiting times were assessed with univariate time-trend analyses and multilevel models. Models were controlled for year of surgery and other patient (age, gender, comorbid conditions, income level, area of residence) and hospital level characteristics (teaching status, procedure volume status). RESULTS: Compared with 1993, median surgical waiting times in the year 2000 increased 36% for patients with breast cancer (to 19 d), 46% with colorectal (to 19 d), 36% with lung (to 34 d) and 4% with prostate cancer (to 83 d). Multilevel models confirmed significant increases in waiting times for all procedures. There were no concerning or consistent differences in waiting times among the categories of hospitals and patients examined. DISCUSSION: There were significant increases in surgical waiting times among patients undergoing breast, colorectal, lung or prostate cancer surgery in Ontario over years 1993-2000. Administrative databases can be used to efficiently measure such waits.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| 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".