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.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".