Acute minor thoracic injuries: evaluation of practice and follow-up in the emergency department.
Bibliographic record
Abstract
OBJECTIVE: To review the management and follow-up of patients with minor thoracic injuries (MTI) treated by emergency or primary care physicians. DESIGN: A multicentre, retrospective study. SETTING: Three university-affiliated emergency departments of the metropolitan region of Quebec city, Que. PARTICIPANTS: Patients older than 16 years of age with suspected or proven rib fractures following traumatic events. MAIN OUTCOME MEASURES: Differences in admission and discharge proportions and disposition management following MTI. RESULTS: Four hundred and forty-seven charts were analyzed. Only 23 patients (5.2%) were admitted during the study period. Admission and discharge proportions were significantly different among the 3 surveyed hospitals, ranging from 1.3% to 15.2% (P < or = .001). There were no recommendations of follow-up noted in most (53.5%) of the charts and there were no differences after hospital stratification. Planned follow-up visits were scheduled for 5.7% of discharged patients. Being older than 65 years of age or having multiple rib fractures had no influence on management and follow-up recommendations. Eighty-two patients (18.6%) had unplanned follow-up visits in the emergency department, with inadequate pain relief as the principal reason for consultation (56.1%). There was no significant difference after stratification for age and type of analgesia. Other clinically significant delayed complications were recorded in 8.3% of all MTI patients. CONCLUSION: The proportion of patients admitted for rib fractures was lower than the expected 25%, based on previous publications, and varied across surveyed hospitals. A very low proportion of patients was offered planned follow-up visits or even any follow-up recommendations in view of possible delayed complications and disabilities. Further studies are needed to identify predictors of delayed MTI complications and enhance appropriate use of follow-up resources.
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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 | 0.002 | 0.001 |
| 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".