Case mix-based changes in health status: A prospective study of elective surgery patients in Vancouver, Canada
Bibliographic record
Abstract
IntroductionHospital activity is often measured using diagnosis-related groups, or case mix groups, but this information does not represent important aspects of patients’ health outcomes. This study reports on case mix-based changes in health status of elective (planned) surgery patients in Vancouver, Canada.Data and methodsWe used a prospectively recruited cohort of consecutive patients scheduled for planned inpatient or outpatient surgery in six acute care hospitals in Vancouver. All participants completed the EQ-5D(5L) preoperatively and 6 months postoperatively, collected from October 2015 to September 2020 and linked with hospital discharge data. The main outcome was whether patients’ self-reported health status improved among different inpatient and outpatient case mix groups.ResultsThe study included 1665 participants with completed EQ-5D(5L) preoperatively and postoperatively, representing a 44.8% participation rate across eight inpatient and outpatient surgical case mix categories. All case mix categories were associated with a statistically significant gain in health status (p < .01 or lower) as measured by the utility value and visual analogue scale score. Foot and ankle surgery patients had the lowest preoperative health status (mean utility value: 0.6103), while bariatric surgery patients reported the largest improvements in health status (mean gain in utility value: 0.1515).ConclusionsThis study provides evidence that it was feasible to compare patient-reported outcomes across case mix categories of surgical patients in a consistent manner across a system of hospitals in one province in Canada. Reporting changes in health status of operative case mix categories identifies characteristics of patients more likely to experience significant gains in health.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".