Clinical outcome analysis for surgical fixation versus conservative treatment on rib fractures: a systematic evaluation and meta-analysis
Bibliographic record
Abstract
BACKGROUND: The efficacy of surgical intervention for traumatic rib fractures in improving clinical outcomes remains a subject of considerable debate. Over the past decade, the adoption of surgical stabilization for rib fractures (SSRF) has increased substantially. This study presents a systematic review and meta-analysis of the literature published over the past 20 years, with the objective of comparing the clinical outcomes of adult patients with multiple traumatic rib fractures who underwent SSRF, relative to those treated conservatively. METHODS: We searched six online databases (PubMed, Web of Science, Embase, Cochrane Library, and the Sino-American Clinical Trials Database) for literature published between June 2004 and June 2024. The Cochrane Collaboration Risk of Bias 2 (RoB 2) and the Newcastle-Ottawa Scale (NOS) tool were employed to assess methodological quality, and relative risks (RR) with 95% confidence intervals (CI) were calculated to evaluate the outcome measures. The primary outcome was all-cause mortality, while the secondary outcomes included hospital length of stay (HLOS), ICU length of stay (ILOS), duration of mechanical ventilation (DMV), and the incidence of pneumonia. Subgroup analyses were performed to assess the effects of fracture type, age, timing of surgical fixation, and study design on treatment outcomes. RESULTS: A total of 47 studies involving 1,078,795 patients were included, consisting of three randomized controlled trials and 44 case-control studies. The results demonstrated that patients who underwent SSRF experienced better outcomes than those receiving conservative treatment in terms of all-cause mortality. However, SSRF was not superior to conservative treatment regarding HLOS, ILOS, or health care costs. Subgroup analyses revealed that the SSRF group had a lower incidence of pneumonia and shorter DMV in patients with flail chest, and patients older than 60 years may also benefit from SSRF, Furthermore, those who underwent SSRF within 72 h had shorter HLOS and DMV compared to those treated conservatively. CONCLUSION: SSRF reduces mortality in patients with multiple rib fractures compared to conservative management, particularly in those with flail chest and in patients over 60 years of age. It also offers benefits in terms of pneumonia incidence and DMV for patients with flail chest. Early SSRF may significantly reduce HLOS and DMV. However, careful screening of appropriate candidates is crucial to maximize the benefits of SSRF.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.013 | 0.021 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".