Using administrative databases to measure waiting times for patients undergoing major cancer surgery in Ontario, 1993-2000.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 it