Effectiveness and Safety of Interventions for Treating Adults with Displaced Proximal Humeral Fracture: A Network Meta-Analysis and Systematic Review
Bibliographic record
Abstract
PURPOSE: Network meta-analysis (NMA) is a comparatively new evidence-based technique in medical disciplines which compares the relative benefits associated with multiple interventions and obtains hierarchies of these interventions for various treatment options. We evaluated the effectiveness and safety of open reduction and internal fixation (ORIF), hemiarthroplasty (HA), reverse shoulder arthroplasty (RSA), intramedullary nailing (IN) and non-operative treatment (NOT) of displaced proximal humeral fractures in adults using Bayesian NMA of data from clinical trials. METHOD: PUBMED, EMBASE and CENTRAL in July 2016 were searched and clinical trials that evaluated interventions for treating adults with displaced proximal humeral fractures were identified. Methodological qualities of studies were assessed by the Newcastle-Ottawa Scale and risk of bias using the Cochrane Collaboration tool. RESULT: Thirty-four trials involving 2165 participants were included in the study. RSA had significantly the highest Constant score and lower total incidence of complications than ORIF, HA and IN. Moreover, RSA resulted in a lower incidence of additional surgery than ORIF and IN. The rank of treatments in terms high Constant score was: RSA, ORIF, IN, NOT and HA. The rank for reduction in total incidence of complications was: RSA, NOT, HA, IN and ORIF. For lowering the risk of additional surgery, the rank was: RSA, NOT, HA, IN and ORIF. CONCLUSION: RSA had the highest probability for improving functional outcome and reduction in the total incidence of complications and requiring additional surgery among the five interventions for treating adults with displaced proximal humeral fracture.
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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 | 0.027 | 0.070 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.045 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| 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".