Comparing two measures of quality of life for children with haemophilia: the CHO‐KLAT and the Haemo‐QoL
Bibliographic record
Abstract
Disease-specific measures of quality of life (QoL) for children with haemophilia are now available for use in clinical studies [Haemophilia, 10, 2004, 9-16]. One of these measures, the Canadian Haemophilia Outcomes - Kids' Life Assessment Tool (CHO-KLAT), was developed in Canada with emphasis on the perspectives of children [Pediatr Blood Cancer, 47, 2006, 305-11; Haemophilia, 10, 2004, 34-43]. Another, the Haemo-QoL, was developed in Europe, with emphasis on the perspectives of clinicians [Haemophilia, 8, 2002, 47-54; Haemophilia, 10, 2004, 17-25]. While these two measures are unique and independent, researchers from both studies were collaboratively linked throughout development and testing. This study presents the results of a joint assessment of the two measures with respect to their strengths, limitations and unique contributions. The primary questions addressed were: 1 What is the relationship between the CHO-KLAT and the Haemo-QoL in terms of summary scores and item content? 2 What are the methodological strengths, limitations and unique contributions of each measure? We conducted a retrospective analysis of data from field testing of both measures. The analysis included a comparative assessment of the basic validity, reliability and items used in each measure. Overall, the CHO-KLAT and the Haemo-QoL are promising and valuable measures of QoL for children with haemophilia. Our analyses confirmed the basic psychometric properties of both tools, but identified some discrepancies between them. Additional data will allow for greater understanding of these discrepancies and lend clarity to how the tools should be used in clinical studies (separately or merged). The present recommendation is that the measures be run independently, but preferably concurrently in studies of children with haemophilia.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".