Clinical outcomes in hemophilia: Towards development of a core set of standardized outcome measures for research
Bibliographic record
Abstract
INTRODUCTION: A lack of uniformity in the choice of outcome measurement in hemophilia care and research has led to studies with incomparable results. We identified a need to define core outcome measures for use in research and clinical care of persons with hemophilia. OBJECTIVE: To move toward a core set of outcome measures for the assessment of persons with hemophilia in research and practice. METHODS: A modified nominal groups process was conducted with an international group of hemophilia experts, including persons with hemophilia as follows. Step 1: item generation for all potential outcome measures. Step 2: survey where respondents voted on the relative importance and usefulness of each item. Steps 3/4: 2-day meeting where attendees voted for items they valued, followed by open discussion and a second round of voting. Step 5: survey where respondents selected their top five items from those with >50% agreement at the meeting. RESULTS: The highest ranked items for the pediatric core set (% agreement) are treatment satisfaction (92.7%), joint health (83.3%), a measure of access to treatment (82.5%), a measure of treatment adherence (72.5%), and generic performance based physical function (72.1%). The highest ranked items for the adult core set (% agreement) are total bleeding events (88.1%), EuroQol five dimensions (85.4%), treatment adherence (82.1%), joint health (79.1%), and number/location of bleeds per unit time (78.6%). CONCLUSION: This process generated a list of preferred outcome measures to consider for assessment in persons with hemophilia. This information now requires refinement to define optimal core sets for use in different clinical/research contexts.
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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 | 0.364 | 0.374 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".