Psychometric validation of the Pyruvate Kinase Deficiency Diary and Pyruvate Kinase Deficiency Impact Assessment in adults in the phase 3 ACTIVATE trial
Bibliographic record
Abstract
BACKGROUND: Pyruvate kinase (PK) deficiency is a rare hereditary disorder characterized by chronic hemolytic anemia and serious sequalae which negatively affect patient quality of life. This study aimed to psychometrically validate the first disease-specific patient-reported outcome (PRO) instruments: the 7-item PK Deficiency Diary (PKDD) and 12-item PK Deficiency Impact Assessment (PKDIA), designed to assess signs, symptoms, and impacts of PK deficiency in patients enrolled in the ACTIVATE global phase 3 study of mitapivat versus placebo (NCT03548220). METHODS: All validation analyses for the PKDD and PKDIA were performed on blinded data, with analyses on item integrity, scoring, reliability, and validity conducted on data from screening and baseline. Completion rates and baseline response distributions were characterized using descriptive statistics. Item response modelling was used to inform a weighted scoring system. Reliability was assessed by internal consistency and test-retest reliability; and validity by convergent and known-groups analyses. RESULTS: Of the 80 adults enrolled, baseline data were available for 77 (96.3%) and 78 (97.5%) patients for the PKDD and PKDIA, respectively. Item responses skewed right, indicating that mean values exceeded median values, especially for items utilizing a 0-10 numeric scale, which were subsequently recoded to a 0-4 scale; 4 items were removed from the PKDIA due to redundancy or low relevance to the trial population. Both the PKDD and PKDIA demonstrated high internal consistency (McDonald's coefficient ω = 0.86 and 0.90, respectively), test-retest reliability (intra-class coefficients of 0.94 and 0.87, respectively), and convergent validity with other PROs (linear correlation coefficients [|r|] between 0.30-0.73 and 0.50-0.82, respectively). CONCLUSIONS: The findings provide evidence of validity and reliability for the PKDD and PKDIA, the first disease-specific PRO measures for PK deficiency, and can therefore increase understanding of, and more accurately capture, the wider impact of PK deficiency on health-related quality of life. Trial registration ClinicalTrials.gov, NCT03548220. Registered June 07, 2018; https://www. CLINICALTRIALS: gov/ct2/show/NCT03548220 .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.046 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".