Health‐related quality‐of‐life questionnaires for deep vein thrombosis and pulmonary embolism: A systematic review on questionnaire development and methodology
Bibliographic record
Abstract
To improve the quality and accuracy of the patient-reported outcome measures that assess health-related quality of life (HRQoL), guidelines have been developed to standardize the development and validation process. Considering the increasing importance of HRQoL questionnaires in research, we set out to review the literature and evaluate whether existing questionnaires developed for deep vein thrombosis (DVT) and pulmonary embolism (PE) fulfill state-of-the-art requirements. The literature search was conducted in March 2019 and updated in September 2020. Seven databases were searched. No time limit was set for the search to include all available questionnaires. The inclusion criteria were original publications describing the development of disease-specific HRQoL questionnaires specific to DVT or PE in adults and available in English. The questionnaires were assessed to determine whether they fulfill the requirements in the latest guidelines. A total of 3826 references were identified. After the exclusion process, 15 papers were reviewed in full, of which 7 were included. Four questionnaires were developed for chronic venous disease, two were specific to DVT, and one was specific to PE. Most questionnaires we found in this review, fulfilled some but none fulfilled all recommendations in existing guidelines. Because the development of current available HRQoL questionnaires specific to DVT or PE do not fulfil all recommendations of existing guidelines, there is room for improvements within this field. Such improvements could likely enhance the quality associated with the use of these end points in clinical trials and practice.
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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 | 0.075 | 0.171 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.017 | 0.020 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".