Prognostic value of platelet indices in patients with acute pulmonary thromboembolism
Bibliographic record
Abstract
Introduction: Given the role of platelets in thrombus formation, markers of platelet activation may be able to predict outcomes in patients with acute pulmonary thromboembolism (PTE). Methods: In a prospective cohort study, 492 patients with acute PTE were enrolled. Patients were evaluated for platelet indices including mean platelet volume (MPV), platelet distribution width (PDW), and platelet-lymphocyte-ratio (PLR), as well as for the simplified Pulmonary Embolism Severity Index (PESI) risk score. The primary endpoint was in-hospital all-cause mortality. Major adverse cardiopulmonary events (MACPE, composite of mortality, thrombolysis, mechanical ventilation and surgical embolectomy during index hospitalization) and all-cause death during follow-up were secondary endpoints. Results: MPV, PDW and PLR were 9.9±1.0 fl, 13.5±6.1%, and 14.7±14.5, respectively, in the total cohort. Whilst MPV was higher in those with adverse events (10.1±1.0 vs 9.9±1.0 fl; P = 0.019), PDW and PLR were not different between two groups. MPV with a cut-off point of 9.85 fl had a sensitivity of 81% and a specificity of 50% in predicting in-hospital mortality, but it had lower performance in predicting MACPE (Area under the curve: AUC 0.58; 95%CI 0.52-0.63) or long-term mortality (AUC 0.54; 95% CI 0.47-0.61). The AUC for all these three markers were lower than the AUC calculated for the simplified PESI score (0.80; 0.71-0.88). Conclusion: Platelet indices had only fair-to-good predictive performance in predicting in-hospital all-cause death. Established PTE risk scoring models such as simplified PESI outperform these indices in predicting adverse outcomes.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".