When is the optimum time for the initiation of early rehabilitative exercise on the postoperative functional recovery of peri-ankle fractures? A network meta-analysis
Bibliographic record
Abstract
Objective: The purpose of this study was to explore the safe and most effective initiation time for the functional recovery of patients with peri-ankle fractures after surgery. Method: We searched electronic databases, including the Cochrane Library, Embase, PubMed and the reference lists of relevant articles published from inception to October 30, 2021. Two researchers independently performed literature screening and data extraction and evaluated the quality of the included literature using the Newcastle-Ottawa Scale. Network meta-analysis, including consistency testing, publication bias, and graphical plotting, was performed using Stata (v16.0). Results: > 0.05). The results of the surface under the cumulative ranking (SUCRA) showed that functional exercise within two days postoperatively may have the lowest VAS scores (SUCRA = 82.8%), functional exercise within 1 week postoperatively may have the lowest deep vein thrombosis rate (SUCRA = 66.8%), functional exercise within 10 days postoperatively may have the fewest total postoperative complication rate (SUCRA = 73.3%) and functional exercise within 12 months postoperatively may contribute to the highest AOFAS scores (SUCRA = 85.5%). Conclusion: The results of this study suggest that initiation of rehabilitation within two days after surgery may be the best time to reduce postoperative pain; rehabilitation interventions within 10 days after surgery may be the optimal time for reducing the total postoperative complication rate, including deep vein thrombosis; and continued functional exercise within 12 months after surgery may steadily and ideally improve the function of the ankle joint.Systematic Review Registration: doi: 10.37766/inplasy2021.12.0030, identifier: INPLASY2021120030.
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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 | 0.023 | 0.044 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.044 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".