Fracture, nonunion and postoperative infection risk in the smoking orthopaedic patient: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract This systematic review and meta-analysis aimed to analyse negative effects of smoking in orthopaedic and trauma patients. A PubMed search was carried out for studies published until July 2020 regarding effects of smoking on fracture risk, nonunion, infection after orthopaedic surgery, and persisting nonunion after scaphoid nonunion surgery. Random effects models calculated for outcome parameters, and relative risks (RR) with 95% confidence intervals are provided. No adjustments for covariates were made. Heterogeneity was assessed with Higgins’ I2, publication bias with Harbord’s p (Hp), sensitivity analysis performed on funnel plots and quality of studies was analysed using the Newcastle-Ottawa Scale. Of 3362 retrieved entries, 69 were included in the final analysis. Unadjusted RR for smokers to develop vertebral (six studies, seven entries; RR: 1.61; p = 0.008; I2 = 89.4%), hip (11 studies, 15 entries; RR: 1.28; p = 0.007; I2 = 84.1%), and other fractures (eight studies, 10 entries; RR: 1.75; p = 0.019; I2 = 89.3%) was significantly higher. Postoperative infection risk was generally higher for smokers (21 studies; RR: 2.20; p < 0.001; I2 = 58.9%), and remained upon subgroup analysis for elective spinal (two studies; RR: 4.38; p < 0.001; I2 = 0.0%) and fracture surgery (19 studies; RR: 2.10; p < 0.001; I2 = 58.5%). Nonunion risk after orthopaedic (eight studies; RR: 2.15; p < 0.001; I2 = 35.9%) and fracture surgery (11 studies; RR: 1.85; p < 0.001; I2 = 39.9%) was significantly higher for smokers, as was persisting nonunion risk after surgery for scaphoid nonunion (five studies; RR: 3.52; p < 0.001; I2 = 0.0%). Sensitivity analysis for each model reduced heterogeneity whilst maintaining significance (all I2 < 20.0%). Smoking has a deleterious impact on fracture incidence, and (subsequent) development of nonunions and postoperative infections. Cite this article: EFORT Open Rev 2021;6:1006-1019. DOI: 10.1302/2058-5241.6.210058
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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 | 0.013 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.040 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| 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".