Assessments of outcome in haemophilia – what is the added value of <scp>QoL</scp> tools?
Bibliographic record
Abstract
INTRODUCTION: Access to treatment and especially to long-term regular replacement treatment with clotting factor concentrates (prophylaxis) have caused dramatic contrasts in the clinical picture between haemophilia populations. An individual patient with severe haemophilia age 20 years can have normal joints or can be severely crippled and unable to work. Assessment of outcome in a standardized way has therefore become essential. AIM: Discuss the relevance and utility of the different outcome assessment tools in patient groups with different access to treatment. METHODS: In the last decade new outcome assessment tools specific for haemophilia have been developed that measure all aspects of health according to the International Classification of Functioning, Disability and Health (ICF) model. These tools are directed at assessing the clinical and radiological status of joints as well as overall functioning, such as participation and psychosocial aspects, evaluating overall health-related quality of life (HRQOL). For deciding which tools to use in clinical practice or research, one needs to consider the specific context with regard to disease burden, healthcare environment and socioeconomic background of the patients being evaluated. CONCLUSION: Prospective systematic assessment of outcome in haemophilia and related bleeding disorders is important. Based upon recent literature a critical appraisal of outcome tools is described.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".