Recovery Patterns
Bibliographic record
Abstract
BACKGROUND: A rise in the proportion of day surgery has seen a concomitant increase in the proportion of patients recovering at home. Blended eHealth is well situated to provide this group with medical support and supervision. However, a data-driven description of the heterogeneity is missing. OBJECTIVE: To identify clinically meaningful patterns of functional recovery following abdominal surgery and describe how the emergent patient characteristics differ between them. METHODS: This was a secondary data analysis of 2 data sets collected through 2 previously conducted RCTs. We used k-medoids clustering and growth mixture modeling on the longitudinal patient-reported outcome measurement information system physical function t-scores of 649 patients. Differences in patient characteristics between the resultant clusters were identified through statistical tests. RESULTS: Three clusters-fast, intermediate, and uneven recovery-were identified regardless of the data set or statistical technique. A fourth cluster-relapse-was identified by both statistical techniques but only in the presence of heavy surgery. The fifth and sixth clusters-low gain and high gain-were identified for both light and heavy surgery, but only through k-medoids clustering. CONCLUSIONS: Trajectories of physical function following abdominal surgery are heterogenous but distinct clinically meaningful patterns can be extracted. This classification may facilitate shared decision-making during preoperative care, and future research may utilize them as targets for prediction.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".