Precision and accuracy of the bedside examination in detecting an ejection fraction of less than 40% following acute myocardial infarction.
Bibliographic record
Abstract
BACKGROUND: Left ventricular function is the most important determinant of prognosis following myocardial infarction. METHODS: A prospective analytical cohort study of 33 cardiac care unit survivors of acute myocardial infarction was performed to assess the accuracy, reproducibility and observer variation of the bedside Valsalva response in predicting an ejection fraction (EF) less than 40%. RESULTS: Agreement between physicians for the clinical Valsalva response was excellent (kappa coefficient 0.75), as was the comparison between physicians' clinical response to the Finapres (Ohmeda, USA) hemodynamic response (weighted kappa=0.85). The EF was significantly higher among patients with a normal Valsalva response (56.9%) than in patients with either an absent overshoot (48.4%) or a square wave (28.3%) response (P<0.001). Physicians were very accurate at estimating whether the EF was greater or less than 40%. In 40 of 66 situations, the clinicians were confident, based on the clinical examination and the Valsalva response, that the EF was either greater or less than 40%. In these situations, agreement (95.0%) and kappa (0.89) were both excellent. When all patients were considered, the degree of agreement (90.8%) and kappa (0.80) diminished slightly. A square wave response had poor sensitivity (37.5%) but excellent specificity (92.7%), whereas any abnormal response had excellent sensitivity (91.7%) but poor specificity (54.8%). CONCLUSION: Compared with many other aspects of the clinical examination, the bedside Valsalva manoeuvre has acceptable degrees of interobserver variability. A normal response tends to rule in an EF of greater than 40%, whereas a square wave response rules out an EF of greater than 40%. This simple bedside manoeuvre may be useful in predicting low EF following acute myocardial infarction.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".