Comparison of Different Clinical Prognostic Scores in Patients with Pulmonary Embolism and Active Cancer
Bibliographic record
Abstract
OBJECTIVE: This article aimed to validate and compare the prognostic performance of generic scores (Pulmonary Embolism Severity Index [PESI] and Hestia) and cancer-specific pulmonary embolism (PE)/venous thromboembolism (VTE) scales (Registro Informatizado de la Enfermedad TromboEmbólica [RIETE], POMPE-C, and modified Ottawa) in PE patients with active cancer. METHODS: A retrospective study was conducted among 460 patients with PE and active cancer. The primary outcome was 30-day overall mortality. Secondary outcomes were 30-day PE-related death and overall adverse outcomes. The prognostic accuracy of clinical scores was determined using receiver operating characteristic (ROC) curve analysis. RESULTS: Within 30 days, 18.0% of patients died, 2.0% suffered major bleeding, and 0.2% presented recurrence of VTE. All scales showed a high area under the ROC curve (AUC) for predicting 30-day overall mortality except modified Ottawa (0.74 [0.70-0.78] for PESI, Hestia, and RIETE; 0.78 (0.74-0.81) for POMPE-C; 0.64 (0.59-0.68) for modified Ottawa]. PESI divided the least patients (9.1%) into low risk, followed by modified Ottawa (17.0%). Hestia stratified the most patients (65.4%) as low risk. But overall mortality of low-risk patients based on these three scales is high (>5%). RIETE and POMPE-C both classified 30.9% of patients as low risk, and low-risk patients stratified by these two scales presented a low overall mortality (1.4 and 3.5%). Similar predictive performance was found for 30-day PE-related death and overall adverse outcomes in these scores. CONCLUSION: Cancer-specific PE prognostic scores (RIETE and POMPE-C) performed better than generic scales (PESI and Hestia) and a cancer-specific VTE prognostic scale (modified Ottawa) in identifying low-risk PE patients with active cancer who may be suitable for outpatient treatment.
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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 | 0.009 |
| 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 | 0.001 |
| 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".