The Contribution of the Subjective Component of the Canadian Pulmonary Embolism Score to the Overall Score in Emergency Department Patients
Bibliographic record
Abstract
BACKGROUND: Clinicians frequently use their experience to determine the pretest probability of pulmonary embolism (PE), although scoring systems are promoted as being more reliable. The Canadian Pulmonary Embolism Score (CPES) combines six objective questions and one subjective question. The CPES has been validated and appears to be useful for risk-stratifying patients. However, research suggests that subjective gestalt performs similarly to the CPES, and the influence of the subjective question on the predictive value of the CPES is not clear. OBJECTIVES: To determine the test characteristics of the CPES, its subjective question, and the degree to which the predictive value of the CPES is influenced by its individual questions. METHODS: The authors performed a prospective observational study on a cohort of emergency department patients suspected of having PE. The authors compared patients' CPES results with the diagnosis of PE, calculated the test characteristics of the CPES, and determined the contribution of individual CPES questions to the score's overall predictive value. RESULTS: Of 607 patients, 61 (10%) had PE. Of low-risk patients (CPES < or =4), 5.54% (n = 449; 95% confidence interval [95% CI] = 3.64% to 8.11%) had PE. The sensitivity (59.0%; 95% CI = 47.4% to 69.8%) and the negative predictive value (94.4%; 95% CI = 92.8% to 95.9%) of the CPES were similar to the sensitivity (53.2%; 95% CI = 40.2% to 65.8%) and negative predictive value (93.5%; 95% CI = 90.7% to 95.5%) of the subjective question alone. In multivariable analysis, nearly all of the predictive value of the CPES was derived from the subjective question. CONCLUSIONS: The predictive value of the CPES appears to be derived primarily from its subjective component.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".