Recognizing the need for personalization of haemophilia patient‐reported outcomes in the prophylaxis era
Bibliographic record
Abstract
The safety and efficacy of treatment options for patients with haemophilia have significantly improved over the last two decades, particularly with greater utilization of prophylactic approaches. Consequently, it is becoming increasingly difficult to differentiate the treatment benefits of available choices based on standard endpoints such as annualized bleeding rates and joint health scores. Patient-reported outcomes (PROs) have shown limited ability to discriminate between treatment outcomes, in part because of their comprehensive nature; i.e. differences in specific outcomes meaningful to individual patients are masked by a global scoring system based on a fixed set of items, many of which may be unimportant for any given patient. There is a clear need for new outcome measures. Initiatives to develop patient-centric outcomes that capture clinically meaningful change are ongoing. One such approach, goal attainment scaling (GAS), allows patients, in collaboration with a trained clinician, to select goals from a medical condition-specific menu of options and subsequently facilitates quantitative assessment of goal realization. Thus, it is fully personalized and sensitive to small, often idiosyncratic, treatment benefits, such as improvements in functional capacity. In this paper, we present the underlying rationale for GAS and one other novel approach to PRO personalization, and discuss their potential to augment current outcome measures by reliably detecting and quantifying treatment effects in individuals with haemophilia on prophylaxis.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| 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; 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".