External validation of the PEGeD diagnostic algorithm for suspected pulmonary embolism in an independent cohort
Bibliographic record
Abstract
Sequential diagnostic algorithms are used in the case of suspected pulmonary embolism (PE). The PEGeD study proposed a new diagnostic strategy to reduce the use of computed tomography pulmonary angiography (CTPA). We aimed to externally validate this diagnostic strategy in an independent cohort. We analyzed data from 3 prospective studies of outpatients with suspected PE. As per the PEGeD algorithm, patients were classified as having a low, moderate, or high clinical pretest probability (C-PTP). PE was excluded with a D-dimer <1000 ng/mL in case of low C-PTP and <500 ng/mL in case of moderate C-PTP. We assessed the yield and safety of this approach and compared them with those of previously validated algorithms. Among the 3308 evaluated patients, 1615 (49%) patients could have had PE excluded according to the PEGeD algorithm, without the need for imaging. Of these patients, 38 (2.3%; 95% confidence interval [CI], 1.7-3.2) were diagnosed with a symptomatic PE at initial testing or during the 3-month follow-up. On further analysis, 36 patients out of these 38 patients had a positive age-adjusted D-dimer. The risk of venous thromboembolic events among the 414 patients with a D-dimer <1000 ng/mL but above the age-adjusted D-dimer cut-off was 36 of 414 (8.7%; 95% CI, 6.4-11.8). We provide external validation of the PEGeD algorithm in an independent cohort. Compared with standard algorithms, the PEGeD decreased the number of CTPA examinations. However, caution is required in patients with a low C-PTP and a D-dimer <1000 ng/mL but above their age-adjusted D-dimer cut-off.
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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 | 0.032 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".