Residual pulmonary vascular obstruction computed with ventilation/perfusion single photon emission computed tomography/computed tomography to predict the risk of venous thromboembolism recurrence in patients with pulmonary embolism: protocol for a cohort study (PRONOSPECT)
Bibliographic record
Abstract
Background: In patients with pulmonary embolism (PE), identifying predictors of recurrence is important to risk-stratify patients and tailor anticoagulation duration. After PE, a significant proportion of patients demonstrate residual pulmonary vascular obstruction (RPVO) on lung imaging. However, the exact prognostic significance of RPVO for venous thromboembolism recurrence remains unclear. Objectives: The primary objective is to assess whether RPVO on ventilation/perfusion (V/Q) single photon emission computed tomography (SPECT)/CT imaging after completion of 3 to 6 months of anticoagulation is an independent predictor of venous thromboembolism recurrence in patients with PE. Methods: The PRONOSPECT trial is a prospective multicenter cohort study. Participants are patients who experienced an objectively proven PE, provoked by a minor transient risk factor or unprovoked; who have been treated with anticoagulant therapy for 3 to 6 uninterrupted months; and for whom anticoagulation will not be prolonged. A standardized baseline patient assessment will be conducted including V/Q SPECT/CT imaging, collection of other potential predictor variables, and a functional evaluation. Anticoagulants will be withdrawn at the 3- or 6-month points from diagnosis and patients will be followed up for up to 2 years. Conclusion: The PRONOSPECT cohort study has the potential to determine whether the presence of RPVO on V/Q SPECT/CT imaging predicts the risk of recurrence in patients with PE in whom there remains a doubt on duration of anticoagulation.
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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 | 0.017 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".