Delayed Tibial Shaft Fracture Healing Associated with Smoking: A Systematic Review and Meta-Analysis of Observational Studies Conducted Worldwide
Bibliographic record
Abstract
Tibial fractures represent a great burden of disease globally, being the most common long-bone fracture; smoking is a known risk factor for delayed skeletal healing and post-fracture complications. This systematic review and meta-analysis aims to analyse the effect of smoking on healing of tibial shaft fractures. PubMed, CINAHL, EMBASE, and Cochrane Library databases were searched from inception to March 2021, with no limitation on language, to find relevant research. All observational studies that assessed the association between cigarette smoking and tibial shaft fracture healing in adults (≥18 years) were included. The quality of studies was evaluated using the Newcastle Ottawa Quality Assessment Scale. A random effects model was used to conduct meta-analysis. Tobacco smoking was associated with an increased rate of non-union and delayed union as well as an increase in time to union in fractures of the tibial shaft. Among the 12 included studies, eight reported an increased rate of non-union, three reported delayed union, and five reported an increase in time to union. However, the results were statistically significant in only three studies for non-union, one for delayed union, and two studies for increased time to union. This review confirms the detrimental impact of smoking on tibial shaft fracture healing and highlights the importance of patient education regarding smoking cessation.
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.020 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".