PB1008 The Diagnostic Performance of the YEARS and PEGeD Algorithm in Patients with Prior Venous Thrombosis Suspected of Pulmonary Embolism
Bibliographic record
Abstract
scan].We performed a multivariable regression analysis using a generalized linear model, adjusting for confounders (age, sex, time of the day, time of the week, study site, D-dimer value).Results: 518,787 patients presented to the study sites during the study and 6,331 received imaging to rule out a PE.The prevalence of PE including a 30day follow-up was 0.10% in the general population, 0.21% in patients presenting with "Chest pain with cardiac features."Overall, the diagnostic yield of imaging was 8.1% (510 positive imaging).The diagnostic yield of imaging for people with the presenting complaint "Chest pain with cardiac features" was 4.5%; for all the other presenting complaints 9.2%, with an adjusted odds ratio of 0.64 (95% confidence interval: 0.46, 0.89, Table 1).Conclusion(s): The threshold for ordering imaging in patients presenting to the ED with chest pain with cardiac features is lower than the in other patients.Interventions aimed at reducing the use of CTPA in the ED should take this into account.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".