Measuring the quality of haemophilia care across different settings: a set of performance indicators derived from demographics data
Bibliographic record
Abstract
BACKGROUND: Haemophilia is a rare disease for which quality of care varies around the world. We propose data-driven indicators as surrogate measures for the provision of haemophilia care across countries and over time. MATERIALS AND METHODS: The guiding criteria for selection of possible indicators were ease of calculation and direct applicability to a wide range of countries with basic data collection capacities. General population epidemiological data and haemophilia A population data from the World Federation of Hemophilia (WFH) Annual Global Survey (AGS) for the years 2013 and 2010 in a sample of 10 countries were used for this pilot exercise. RESULTS: Three indicators were identified: (i) the percentage difference between the observed and the expected haemophilia A incidence, which would be close to null when all of the people with haemophilia A (PWHA) theoretically expected in a country would be known and reported to the AGS; (ii) the percentage of the total number of PWHA with severe disease; and (iii) the ratio of adults to children among PWHA standardized to the ratio of adults to children for males in the general population, which would be close to one if the survival of PWHA is equal to that of the general population. Country-specific values have been calculated for the 10 countries. CONCLUSIONS: We have identified and evaluated three promising indicators of quality of care in haemophilia. Further evaluation on a wider set of data from the AGS will be needed to confirm their value and further explore their measurement properties.
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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 | 0.011 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".