Predictors requiring special attention to prevent clavicle fracture nonunion: a systematic review of literature
Bibliographic record
Abstract
Background: Clavicle fracture (CF) is the tenth most prevalent fracture, accounting for an annual incidence of 37/10,000. This systematic review highlights the factors contributing to the nonunion union of the clavicular fracture. Method: A systematic search was conducted using three web-based databases up to August 12, 2022, for conducting qualitative analysis. Articles were screened for relevance, and only studies that met inclusion criteria based on PECOS; P (patients): participants diagnosed with clavicular fracture; E (exposure): nonunion, C (control): not applicable; O (outcomes): factors contributing to nonunion or delayed union; S (studies): trials and observational studies. The Newcastle-Ottawa Scale was used to assess the quality of the cohort studies. The Cochrane risk of bias tool was used to assess the bias in randomized control trials. Results: Ten studies were selected after the final literature search. Two thousand seven hundred and sixty-six adult participants who were radiologically and clinically diagnosed with nonunion clavicular fracture were included to pool the qualitative results. Fall was the most dominant cause of clavicular fracture, followed by road traffic collisions. Open reduction was widely used to treat nonunion correction. The qualitative results suggested a prominent correlation of nonunion with advancing age, female gender, high energy trauma, high Disabilities of the Arm, Shoulder, and Hand Score, smoking, fracture displacement, clavicular shortening, the callus on radiography, and fracture movement. The mid-shaft fracture was the most dominant type of fracture in the included studies; highly associated with nonunion in comparison to medial or lateral CF. The previous history of operation was an independent factor contributing to nonunion. Conclusion: The results of this systematic review suggested the predictors contributing to nonunion in the CF. Demographic factors such as advancing age with female gender are at higher risk of developing clavicular nonunion. Smoking was the most dominantly highlighted environmental factor contributing to nonunion. Diaphyseal or midshaft fracture was the most common site for nonunion. Therefore, we suggested that patients with the predictors mentioned above require special attention to prevent nonunion of the CFs. More studies should be conducted on this subject to assess the factors that pose a risk associated with the nonunion of the bone for better clinical management and outcomes of the 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 | 0.011 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".