DETERMINING THE BIAS IN WAITING TIMES REPORTED BY THE ONTARIO JOINT REPLACEMENT REGISTRY
Bibliographic record
Abstract
Ontario Ministry of Health and Long-Term Care (MOHLTC) established the Ontario Joint Replacement Registry (OJRR) in April 2000 to provide information needed to assess waiting times for Total Joint Replacement (TJR) surgery on an ongoing basis. Therefore a major concern of the OJRR is the quality of its waiting time data. The primary objective of this study was to determine the quality of the OJRR waiting time data by measuring the bias in these two waiting time periods: referral to surgery and decision to surgery. The mean difference method was used to estimate bias in its waiting times reported as a continuous variable and the McNemar test was used to estimate bias in its waiting times reported as a dichotomous variable. In continuous form, bias in OJRR waiting times was estimated as a relatively small point estimate (i.e. 0.13 and 0.66 weeks for decision and referral to surgery, respectively); but with considerable variability associated with these values (i.e. +/- half a year). As a binary variable, indicating if surgery was received within a waiting time threshold, no bias was found. Our results suggested that better definitions for waiting time date fields, especially for decision date and referral date fields, may be in order.
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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 | 0.065 | 0.257 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".