Development of a disease-specific measure of quality of life in myelodysplastic syndromes (MDS): The “QUALMS-1”.
Bibliographic record
Abstract
6103 Background: Studies assessing the quality of life (QoL) experienced by patients with MDS have almost universally relied upon generic measures; however, disease-specific QoL tools can allow for more sensitive assessments of the impact of changes in disease status. Methods: Using a clinical impact method of instrument development, individual and combined focus groups were conducted with 32 members of our institution’s MDS community (patients, their caregivers, and health care providers) to identify MDS-relevant QoL domains and associated question topics. Participants’ rankings of the importance of the domains and question topics were compared, collapsing patients/caregivers into one group and physicians/other providers into another. A draft scale was constructed taking a greater number of questions from the more highly-ranked domains. Results: “Fatigue” was ranked as the most important domain (see table). None of the 12 domains were ranked significantly differently by patients/caregivers versus providers. The two groups ranked 5 of 60 question topics differently: “Too tired for routine tasks” (providers higher; p= .05); “limited availability of support beyond the family” (providers higher; p= .02); “organizing life around transfusion/MD appointments” (providers higher, p= .03); “bruising” (patients/caregivers higher, p= .05) and “anger over diagnosis” (providers higher, p= .03). Conclusions: A high level of agreement in the rankings of domains and question topics between MDS patients/caregivers and providers suggests that the QoL experience of MDS patients is consistently compromised. The resulting 38-item draft QUALMS-1 tool is now being piloted (cognitive debriefing and behavioral coding) in a new cohort of MDS patients, with the ultimate goal of validation in a multi-institutional setting. [Table: see text]
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".