Quality of life in thalassemia: A comparison of SF‐36 results from the thalassemia longitudinal cohort to reported literature and the US norms
Bibliographic record
Abstract
Thalassemia is a chronic, inherited blood disorder, which, in its most severe form, causes life-threatening anemia. Advances in treatment have led to increased life expectancy however the need for chronic blood transfusions and chelation therapy remains a significant burden for patients. Our study compared health related quality of life (HRQOL) from the Thalassemia Clinical Research Network's (TCRNs) Thalassemia Longitudinal Cohort (TLC) study to US norms and assessed association with clinical variables. There were 264 patients over age 14 who completed the Medical Outcomes Study 36-Item Short Form Health Survey version 2 (SF36v2) baseline assessment. When compared to US norms, TLC patients had statistically significant (P < 0.05) worse HRQOL on five of the eight subscales (physical functioning, role-physical, general health, social functioning, and role-emotional) and on both summary scales (physical component summary and mental component summary). Women, older patients, and those with more disease complications and side effects from chelation reported lower HRQOL. In general, adolescents and adults with thalassemia report worse HRQOL than the US population, despite contemporary therapy. The SF-36 should become a standard instrument for assessing HRQOL in thalassemia to determine predictors of low HRQOL which may be better addressed by a multidisciplinary team.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 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".