Days at home after surgery as a perioperative outcome: scoping review and recommendations for use in health services research
Bibliographic record
Abstract
BACKGROUND: Days at home after surgery is a promising new patient-centred outcome metric that measures time spent outside of healthcare institutions and mortality. The aim of this scoping review was to synthesize the use of days at home in perioperative research and evaluate how it has been termed, defined, and validated, with a view to inform future use. METHODS: The search was run on MEDLINE, Embase, and Scopus on 30 March 2023 to capture all perioperative research where days at home or equivalent was measured. Days at home was defined as any outcome where time spent outside of hospitals and/or healthcare institutions was calculated. RESULTS: A total of 78 articles were included. Days at home has been increasingly used, with most studies published in 2022 (35, 45%). Days at home has been applied in multiple study design types, with varying terminology applied. There is variability in how days at home has been defined, with variation in measures of healthcare utilization incorporated across studies. Poor reporting was noted, with 14 studies (18%) not defining how days at home was operationalized and 18 studies (23%) not reporting how death was handled. Construct and criterion validity were demonstrated across seven validation studies in different surgical populations. CONCLUSION: Days at home after surgery is a robust, flexible, and validated outcome measure that is being increasingly used as a patient-centred metric after surgery. With growing use, there is also growing variability in terms used, definitions applied, and reporting standards. This review summarizes these findings to work towards coordinating and standardizing the use of days at home after surgery as a patient-centred policy and research tool.
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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 | 0.181 | 0.423 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.016 | 0.016 |
| Bibliometrics | 0.058 | 0.045 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.016 | 0.020 |
| Open science | 0.009 | 0.010 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".