Equity in waiting times for major joint arthroplasty.
Bibliographic record
Abstract
OBJECTIVE: To ascertain whether waiting lists are managed in an equitable fashion in a universal health system by examining demographic, socioeconomic and clinical factors, along with 2 health systems variables. DESIGN: A prospective survey by questionnaire. SETTING: The Capital Health Region of Edmonton, Alta. PATIENTS AND METHODS: A cohort of 553 patients, who were waiting for either total hip or total knee replacement surgery, seen between Dec. 18, 1995, and Jan. 24, 1997. INTERVENTIONS: A home visit was made when the patient was first placed on the waiting list and again just before surgery to complete the questionnaires. The Western Ontario and McMaster Universities (WOMAC) instrument and the Medication Quantification Score were administered at the time the patient was placed on the waiting list. MAIN OUTCOME MEASURE: The length of waiting time, defined as the date the patient was put on the waiting list to the date the patient was operated on. RESULTS: There were no biases in waiting time with respect to age, gender, education or work status. Although pain and function were not related to waiting time, multivariate analyses found that marital status, primary language, body mass index, pain medication use and the size of the surgeons' major joint replacement practice determined waiting time for surgery. However, this model explained only 10% of the variance in waiting time. CONCLUSION: Waiting lists were managed unfairly in terms of clinical equity (clinical severity) but managed fairly in terms of social equity.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".