Surgical Fixation vs Nonoperative Management of Flail Chest: A Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Flail chest is a life-threatening injury typically treated with supportive ventilation and analgesia. Several small studies have suggested large improvements in critical care outcomes after surgical fixation of multiple rib fractures. The purpose of this study was to compare the results of surgical fixation and nonoperative management for flail chest injuries. STUDY DESIGN: A systematic review of previously published comparative studies using operative and nonoperative management of flail chest was performed. Medline, Embase, and the Cochrane databases were searched for relevant studies with no language or date restrictions. Quantitative pooling was performed using a random effects model for relevant critical care outcomes. Sensitivity analysis was performed for all outcomes. RESULTS: Eleven manuscripts with 753 patients met inclusion criteria. Only 2 studies were randomized controlled designs. Surgical fixation resulted in better outcomes for all pooled analyses including substantial decreases in ventilator days (mean 8 days, 95% CI 5 to 10 days) and the odds of developing pneumonia (odds ratio [OR] 0.2, 95% CI 0.11 to 0.32). Additional benefits included decreased ICU days (mean 5 days, 95% CI 2 to 8 days), mortality (OR 0.31, 95% CI 0.20 to 0.48), septicemia (OR 0.36, 95% CI 0.19 to 0.71), tracheostomy (OR 0.06, 95% CI 0.02 to 0.20), and chest deformity (OR 0.11, 95% CI 0.02 to 0.60). All results were stable to basic sensitivity analysis. CONCLUSIONS: The results of this meta-analysis suggest surgical fixation of flail chest injuries may have substantial critical care benefits; however, the analyses are based on the pooling of primarily small retrospective studies. Additional prospective randomized trials are still necessary.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.053 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".