Retrospective Analysis of the Computed Tomography Pulmonary Angiogram Utilization Patterns in the Emergency Department
Bibliographic record
Abstract
OBJECTIVES: Guidelines and high-quality studies recommend using clinical decision-making (CDM) tools over clinical gestalt when evaluating a patient for pulmonary embolism. The purpose of this study is to investigate our computed tomography pulmonary angiogram (CTPA) utilization patterns and identify causal factors. METHODS: A retrospective cohort study of CTPA studies ordered by emergency physicians in January, April, July, and October 2017 was undertaken. All necessary information to categorize patients by Wells' score, revised Geneva score, and pulmonary embolism rule-out criteria (PERC) was collected. In addition, various bloodwork, chest radiograph, and computed tomography results were collected. This data was analysed by the Pearson chi-square test or Fisher's exact test for categorical data and independent-samples t test for continuous variables. RESULTS: A total of 510 CTPA studies were performed, with a mean age was 61.6 and a 50.6% female population. 136 studies (26.7%) failed to appropriately follow any CDM tool. CDM tool failure rate was dependent on whether the study was ordered from a community (14.9%) or tertiary hospital (University of Alberta Hospital, 27.9% and Royal Alexandra Hospital, 24.6%) (P = .038). Of these 136 studies, 31 were low/moderate risk and the d-dimer was negative. The remainder were either PERC-negative or low/moderate risk without d-dimer performed. The cumulative positive pulmonary embolism rate was 12.5%. With utilization of a CDM tool, the positive pulmonary embolism rate was 15.0%, compared to 5.9% when using gestalt (P = .026). CONCLUSIONS: This study confirms a high rate of CDM tool use failure, and a higher positive CTPA rate for CDM tools compared to clinical gestalt.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".