Emergency Medicine Practitioner Knowledge and Use of Decision Rules for the Evaluation of Patients with Suspected Pulmonary Embolism: Variations by Practice Setting and Training Level
Bibliographic record
Abstract
BACKGROUND: Several clinical decision rules (CDRs) have been validated for pretest probability assessment of pulmonary embolism (PE), but the authors are unaware of any data quantifying and characterizing their use in emergency departments. OBJECTIVES: To characterize clinicians' knowledge of and attitudes toward two commonly used CDRs for PE. METHODS: By using a modified Delphi approach, the authors developed a two-page paper survey including 15 multiple-choice questions. The questions were designed to determine the respondents' familiarity, frequency of use, and comprehension of the Canadian and Charlotte rules. The survey also queried the frequency of use of unstructured (gestalt) pretest probability assessment and reasons why physicians choose not to use decision rules. The surveys were sent to physicians, physician assistants, and medical students at 32 academic and community hospitals in the United States and the United Kingdom. RESULTS: Respondents included 555 clinicians; 443 (80%) work in academic practice, and 112 (20%) are community based. Significantly more academic practitioners (73%) than community practitioners (49%) indicated familiarity with at least one of the two decision rules. Among all respondents familiar with a rule, 50% reported using it in more than half of applicable cases. A significant number of these respondents could not correctly identify a key component of the rule (23% for the Charlotte rule and 43% for the Canadian rule). Fifty-seven percent of all respondents indicated use of gestalt rather than a decision rule in more than half of cases. CONCLUSIONS: Academic clinicians were more likely to report familiarity with either of these two specific decision rules. Only one half of all clinicians reporting familiarity with the rules use them in more than 50% of applicable cases. Spontaneous recall of the specific elements of the rules was low to moderate. Future work should consider clinical gestalt in the evaluation of patients with possible PE.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".