Investigation of myocardial contusion with sternal fracture in the emergency department
Bibliographic record
Abstract
Objective To describe the use of initial electrocardiogram (ECG), follow-up ECG or equivalent monitoring, and troponin I in patients presenting with sternal fracture who are assessed in emergency departments or by front-line physicians. Design Multicentre descriptive retrospective study. Setting Two traumatology teaching centres in Quebec city, Que. Participants Fifty-four trauma patients presenting with sternal fracture. Interventions Assessment of the use of initial ECG, ECG or equivalent monitoring 6 hours after trauma, and troponin administration. Main outcome measures In terms of ECG use, quality comparison criteria were selected on the basis of expert opinions in 4 studies. An initial ECG and a follow-up ECG 6 hours after trauma or cardiac monitoring 6 hours after trauma were recommended by most authors for diagnosing myocardial contusion in cases of sternal fracture. Serum troponin I administered 4 to 8 hours after chest trauma was also recommended by some as an effective means of detecting substantial arrhythmia secondary to myocardial contusion. Descriptive univariate analyses and ![Graphic][1] tests were performed. A P < .05 was considered significant. Results Thirty-nine patients (72%) were assessed initially with ECGs; after 6 hours in the emergency department, 18 of these patients (33%) had follow-up ECGs or equivalent cardiac monitoring. Sixteen patients (30%) were assessed by means of troponin I dosage. Two patients (4%) presented with ECG abnormalities and only 1 patient (2%) presented with an elevated troponin I level. Conclusion Emergency physicians must increase their use of ECG in initial or follow-up diagnosis for trauma patients presenting with sternal fracture to detect myocardial contusion and arrhythmia. The use of troponin in conjunction with ECG is also suggested for this population in order to identify patients at risk of complications secondary to myocardial contusion. [1]: /embed/inline-graphic-1.gif
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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 | 0.000 | 0.000 |
| 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".