Operative Stabilization of Flail Chest Injuries Reduces Mortality to That of Stable Chest Wall Injuries
Bibliographic record
Abstract
OBJECTIVE: To determine the prevalence, management and outcomes of patients with flail chest injuries, compared to patients without flail chest injuries (single rib fractures and multiple rib fractures without a flail segment). DESIGN: Retrospective cohort study. SETTING: Ontario, Canada. PARTICIPANTS: Ontario residents over the age of 16 years who had been admitted to hospital with a chest wall injury from 2004 to 2015 were identified using administrative health care databases. MAIN OUTCOME MEASUREMENTS: Outcomes included treatment modalities such as rate of surgical repair, days on mechanical ventilation, days in the intensive care unit, days in hospital, rate of chest tube placement; and rates of complication, including pneumonia, tracheostomy, readmission, and death. RESULTS: In total 117,204 patients with fractures of the chest wall were identified. Of the entire cohort, 1.5% of them had a flail chest injury, 41% had multiple rib fractures, and 58% had single rib fractures. Patients with flail chest injuries had significantly worse outcomes compared to patients with multiple rib fractures in all categories (P < 0.0001). Similarly, patients with multiple rib fractures had significantly worst outcomes compared with patients with single rib fractures (P < 0.0001). Only 4.5% of patients with flail chest injuries were treated surgically, however, the number increased from 1% before 2010 to 10% after 2010 (P < 0.0001). After adjustment for potential confounders, patients with flail chest injuries treated surgically had a reduced risk of early mortality compared to those treated nonoperatively (OR 0.16, P = 0.019). CONCLUSIONS: Surgical stabilization of flail chest injuries has increased significantly in recent years. The results of this study provide preliminary evidence that the increasing rate of surgical intervention may be warranted by reducing mortality. LEVEL OF EVIDENCE: Therapeutic Level II. See Instructions for Authors for a complete description of levels of evidence.
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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 | 0.000 | 0.003 |
| 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) | 0.002 | 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 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".