Surgical stabilization of rib fractures for flail chest: Analysis of center-based variability in practice and outcomes
Bibliographic record
Abstract
BACKGROUND: Given the lack of high-quality data on patient selection for surgical stabilization of rib fractures (SSRF), significant variability in practice likely exists across trauma centers. We aimed to determine whether centers with a more liberal approach to SSRF had improved outcomes. METHODS: We performed a retrospective cohort study of adult patients with flail chest admitted to Level I or II trauma centers participating in the American College of Surgeons' Trauma Quality Improvement Program. The primary outcome was hospital mortality; secondary outcomes included discharge status, tracheostomy, duration of mechanical ventilation, and hospital length of stay. Logistic regression was performed to calculate center-level observed/expected rates of SSRF and centers were grouped into quintiles from "most liberal" to "most restrictive." Multivariable regression was used to determine the association between these quintiles and outcomes. We also used an instrumental variable analysis to evaluate the association between SSRF and mortality at the patient level. RESULTS: Among 23,619 patients with flail chest across 354 centers, 22% underwent SSRF. Center rates of fixation ranged from 0% to 88%. Higher rates of SSRF were not associated with lower mortality overall (highest vs. lowest quintile: odds ratio, 0.86; 95% confidence interval, 0.63-1.17). However, centers with a more liberal approach to SSRF had lower rates of independent status at discharge, higher tracheostomy rates, longer duration of mechanical ventilation, and longer hospital and ICU length of stay. The patient level analysis demonstrated that SSRF as was associated with a 25% lower risk of death. CONCLUSION: Overall, centers with a liberal approach to SSRF do not show improved outcomes among patients with a flail chest, but have higher resource utilization. Results at the patient level suggest that there is a population likely to benefit but these patients remain to be identified through further research. LEVEL OF EVIDENCE: Prognostic and Epidemiological; Level III.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".