Development of the pyruvate kinase deficiency diary and pyruvate kinase deficiency impact assessment: Disease‐specific assessments
Bibliographic record
Abstract
INTRODUCTION: Currently recommended patient-reported outcome (PRO) measures for patients with pyruvate kinase (PK) deficiency are non-disease-specific. The PK Deficiency Diary (PKDD) and PK Deficiency Impact Assessment (PKDIA) were developed to be more targeted measures for capturing the symptoms and impacts of interest to this patient population. METHODS: The instruments were developed based on concept elicitation interviews with 21 adults and modified based on 20 cognitive interviews. The domain structure and item concepts of the PKDD and PKDIA were compared with currently recommended measures, the EORTC QLQ-C30 and the SF-36v2®. RESULTS: The PKDD is a seven-item measure of the core signs and symptoms of PK deficiency. The PKDIA is a 14-item measure of the impacts of PK deficiency on patients' health-related quality of life (HRQoL). Minimal similarities were found between the new measures and the EORTC QLQ-C30 (eg, 43% of concepts were similar to the PKDD; 42% were similar to the PKDIA) and SF-36v2® (57% of concepts were similar to the PKDD; 17% were similar to the PKDIA). CONCLUSIONS: The PKDD and PKDIA fill a gap in the existing outcomes measurement strategy for PK deficiency. Future work includes psychometric evaluation of these newly developed measures.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".