Use of patient-reported outcome measures in patients with venous thromboembolism: communication from the ISTH SSC Subcommittee on Predictive and Diagnostic Variables in Thrombotic Disease
Bibliographic record
Abstract
Patient-reported outcome measures (PROMs) are patient-completed instruments that capture patient-perceived health status and well-being. PROMs measure disease impact and outcomes of care as reported by those who experience the disease. After pulmonary embolism or deep vein thrombosis, patients may face a broad spectrum of complications and long-term sequelae beyond the usual quality-of-care indicators of recurrent venous thromboembolism (VTE), bleeding complications, and survival. The full impact of VTE on individual patients can only be captured by assessing all relevant health outcomes from the patient's perspective in addition to the traditionally recognized complications. Defining and measuring all important outcomes will help facilitate treatment tailored to the needs and preferences of patients and may improve health outcomes. The International Society on Thrombosis and Haemostasis Scientific and Standardization Committee Subcommittee on Predictive and Diagnostic Variables in Thrombotic Disease endorsed the International Consortium for Health Outcomes Measurement (ICHOM) VTE project on development of a standardized set of patient-centered outcome measures for patients with VTE. In this communication, the course and result of the project are summarized, and based on these findings, we propose recommendations for the use of PROMs during clinical follow-up of patients with VTE. We describe challenges to implementation of PROMs and explore barriers and enablers.
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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 | 0.142 | 0.235 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| 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".