The feasibility, acceptability, safety, and effects of early weight bearing in humeral fractures – a scoping review
Bibliographic record
Abstract
PURPOSE: Non-weight bearing is often recommended after humeral fractures. This review aims to summarise the extent and nature of the evidence for the feasibility, acceptability, safety, and effects of early weight bearing (EWB) in people with humeral fractures, treated operatively or non-operatively. . METHODS: Data sources identified published (PUBMED, EMBASE, CINAHL) and unpublished (ClinicalTrials.gov, CENTRAL, NIHR Open Research, OpenGrey) literature. Independent data extraction was conducted by two reviewers. RESULTS: 13 901 records were retrieved. Ten studies, involving 515 post-operative patients and 351 healthcare professionals, were included. EWB was found to be feasible in nine studies. There was limited evidence regarding adherence to EWB. Trauma and orthopaedic surgeons reported that EWB was acceptable. This depended on surgery type and whether it was a post-operative polytrauma case. No acceptability data was reported from patients' perspectives. Only one study reported two patients who developed unsatisfactory outcomes from excessive post-operative EWB. Positive effects of EWB were reported on disability level, pain, shoulder and elbow motion, and union. CONCLUSION: There is some evidence for the feasibility, safety, and effectiveness of post-operative EWB after humeral fractures. There was limited data on the acceptability of EWB. Heterogeneous study designs, and variations in EWB protocols limit conclusions.
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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 | 0.021 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".