Psychometric Evaluation of ITP Life Quality Index (ILQI) in a Global Survey of Patients with Immune Thrombocytopenia
Bibliographic record
Abstract
INTRODUCTION: Immune thrombocytopenia (ITP) is an autoimmune disorder caused by immunologic destruction of otherwise normal platelets. Patients and physicians differ in their views pertaining to the limitations imposed on patients' daily lives by ITP and its treatment. Poor understanding of ITP symptoms can result in misdiagnosis and complex treatment patterns, and affect patient health-related quality of life (HRQoL). The ITP Life Quality Index (ILQI) is a 10-item patient-reported outcome measure developed for clinical practice to aid discussions between patients and physicians. This research aimed to validate the psychometric properties of the ILQI using data collected in the ITP World Impact Survey (I-WISh). METHODS: I-WISh data containing responses to the ILQI from 1507 patients with ITP across 13 countries worldwide was subject to psychometric analysis to evaluate the structure, reliability and validity of the ILQI and assess scoring cut-offs. RESULTS: The ILQI has an overarching unidimensional structure, supporting a total score including all 10 items. Reliability was supported (Cronbach's alpha = 0.90). ILQI scores monotonically increased with ITP severity. ILQI scores correlated with measures of fatigue and emotional well-being, supporting construct validity. Differential item functioning (DIF) analyses showed that ILQI item responses were interpreted similarly between the USA and other Western countries. It was suggested that previous clinical cut-off score of 20 for "impaired HRQoL" was reduced to 17 and a cut-off of 23-25 (rather than 30) was suggested to assess "significantly impaired HRQoL". CONCLUSION: The validity and reliability of the ILQI to assess HRQoL of patients with ITP is supported. The revised cut-off scores for the ILQI will aid patient-centric decision-making.
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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".