How well does the Canadian haemophilia Outcomes‐Kids' Life Assessment Tool (CHO‐KLAT) measure the quality of life of boys with haemophilia?
Bibliographic record
Abstract
BACKGROUND: It is important to measure the quality of life (QoL) of boys with haemophilia, because the diagnosis has a significant impact on their lives and this impact fluctuates over time. A disease-specific measure of QoL is required because the aspects of life that are affected by haemophilia may differ from those assessed by generic QoL measures. This paper describes the final phase of development of a disease-specific measure of QoL for boys with haemophilia: the Canadian Haemophilia Outcomes-Kids Life Assessment Tool (CHO-KLAT). PROCEDURE: A 79-item version of the CHO-KLAT was administered to 52 children. A detailed item analysis was conducted to shorten the CHO-KLAT. The reliability of the revised version was assessed using intraclass correlation coefficients. Validity was assessed by comparing it to the PedsQL and the HaemoQoL. RESULTS: The item analysis resulted in the retention of 35 strongly performing items (CHO-KLAT(35)). These items were aggregated into the CHO-KLAT(35) summary score. Repeated measures reliability of the CHO-KLAT(35) was 0.74 for children and 0.83 for parents, and the child-parent concordance was 0.75. The validity of the CHO-KLAT(35) was confirmed by a correlation of 0.78 with the Haemo-QoL and of 0.59 with the PedsQL. CONCLUSIONS: The CHO-KLAT(35) is a reliable and valid measure of QoL for boys with haemophilia.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".