Improving the quality of care with a single-entry model of referral for total joint replacement: a preimplementation/postimplementation evaluation
Bibliographic record
Abstract
OBJECTIVES: We assessed: (1) waiting time variation among surgeons; (2) proportion of patients receiving surgery within benchmark and (3) influence of the Winnipeg Central Intake Service (WCIS) across five dimensions of quality: accessibility, acceptability, appropriateness, effectiveness, safety. DESIGN: Preimplementation/postimplementation cross-sectional design comparing historical (n=2282) and prospective (n=2397) cohorts. SETTING: Regional, provincial health authority. PARTICIPANTS: Patients awaiting total joint replacement of the hip or knee. INTERVENTIONS: The WCIS is a single-entry model (SEM) to improve access to total hip replacement (THR) or total knee replacement (TKR) surgery, implemented to minimise variation in total waiting time (TW) across orthopaedic surgeons and increase the proportion of surgeries within 26 weeks (benchmark). Impact of SEMs on quality of care is poorly understood. PRIMARY AND SECONDARY OUTCOME MEASURES: Primary outcomes related to 'accessibility': waiting time variation across surgeons, waiting times (Waiting Time 2 (WT2)=decision to treat until surgery and TW=total waiting time) and surgeries within benchmark. Analysis included descriptive statistics, group comparisons and clustered regression. RESULTS: Variability in TW among surgeons was reduced by 3.7 (hip) and 4.3 (knee) weeks. Mean waiting was reduced for TKR (WT2/TW); TKR within benchmark increased by 5.9%. Accessibility and safety were the only quality dimensions that changed (post-WCIS THR and TKR). Shorter WT2 was associated with post-WCIS (knee), worse Oxford score (hip and knee) and having medical comorbidities (hip). Meeting benchmark was associated with post-WCIS (knee), lower Body Mass Index (BMI) (hip) and worse Oxford score (hip and knee). CONCLUSIONS: The WCIS reduced variability across surgeon waiting times, with modest reductions in overall waits for surgery. There was improvement in some, but not all, dimensions of quality.
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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 | 0.028 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".