Validation of days at home as an outcome measure after surgery: a prospective cohort study in Australia
Bibliographic record
Abstract
Objective To evaluate ‘days at home up to 30 days after surgery’ (DAH 30 ) as a patient-centred outcome measure. Design Prospective cohort study. Data source Using clinical trial data (seven trials, 2109 patients) we calculated DAH 30 from length of stay, readmission, discharge destination and death up to 30 days after surgery. Main outcome The association between DAH 30 and serious complications after surgery. Results One or more complications occurred in 263 of 1846 (14.2%) patients, including 19 (1.0%) deaths within 30 days of surgery; 245 (11.6%) patients were discharged to a rehabilitation facility and 150 (7.1%) were readmitted to hospital within 30 days of surgery. The median DAH 30 was significantly less in older patients (p<0.001), those with poorer physical functioning (p<0.001) and in those undergoing longer operations (p<0.001). Patients with serious complications had less days at home than patients without serious complications (20.5 (95% CI 19.1 to 21.9) vs 23.9 (95% CI 23.8 to 23.9) p<0.001), and had higher rates of readmission (16.0% vs 5.9%; p<0.001). After adjusting for patient age, sex, physical status and duration of surgery, the occurrence of postoperative complications was associated with fewer days at home after surgery (difference 3.0(95% CI 2.1 to 4.0) days; p<0.001). Conclusions DAH 30 has construct validity and is a readily obtainable generic patient-centred outcome measure. It is a pragmatic outcome measure for perioperative clinical trials.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".