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Factors Impacting Quality of Life in Thalassemia Patients; Results from the Intercontinenthal Collaborative Study

2016· article· en· W2771573185 on OpenAlexaffabout
Ali Amid, Rebecca Leroux, Manuela Merelles‐Pulcini, Saeed Yassobi, Antoine N. Saliba, Richard Ward, Mehran Karimi, Alì Taher, Robert J. Klaassen, Melanie Kirby‐Allen

Bibliographic record

VenueBlood · 2016
Typearticle
Languageen
FieldMedicine
TopicHemoglobinopathies and Related Disorders
Canadian institutionsChildren's Hospital of Eastern OntarioUniversity of OttawaUniversity of TorontoToronto General HospitalUniversity Health NetworkSickKids FoundationHospital for Sick Children
Fundersnot available
KeywordsMedicineThalassemiaQuality of life (healthcare)DiseaseBlood transfusionAnemiaPediatricsInternal medicineNursing

Abstract

fetched live from OpenAlex

Abstract Thalassemia is one of the most common causes of inherited anemia worldwide. While significant advances has been made in clinical management of thalassemia patients over the past few decades, our knowledge on the factors affecting the quality of life of thalassemia patients is limited. The "IntercontinThal Study" is a collaborative effort to study the quality of life (QoL) and quality of care of thalassemia patients in populations across diverse social and health care systems. Data presented here are from the three participating centers in Canada, Lebanon and Iran. We have gathered study data through: a) QoL questionnaire SF-36 completed by patients, b) a specifically designed and validated questionnaire completed by patients which addressed patient's social status (marriage/relationship status, education, employment status, and access to social support and health care), and c) review of the patients' charts using a data collection form. This form included: patients' demographics, specifics of transfusion therapy and iron chelation, thalassemia-related and other clinical complications (endocrinopathies, bone disease, cardiac disease, hemolysis-related complications, etc.), tissue iron content (liver and cardiac) and/or serum ferritin within the past three years, and splenectomy status. All study questionnaires were translated into Persian (for Iranian patients) and Arabic (for Lebanese patients). Due to the variety of the clinical complications, all clinical complications were aggregated together for statistical analysis. We used univariate and multivariate regression analysis to study the association of predictors and patients' QoL Mental Component Summary (MCS) Score. Ninety seven patients [46 female, 59 transfusion-dependent beta-thalassemia (TDT) and 38 non-transfusion-dependent beta-thalassemia (NTDT)] were included in the analysis. All patients were older than 18 years of age (Mean 32 years, SD: 7 years). In univariate analysis age, access to social support and health care, marriage status, liver iron concentration and ferritin (strongly correlated with each other), and disease-related complications were found to be predictor of QoL MCS scores. In NTDT patients, splenectomy and lower baseline hemoglobin were also significantly associated with reduced QoL. In multivariate analysis, ferritin and age (and clinical complications in TDT patients) were found to independently be associated with reduced QoL. LIC was not found to be an independent factor likely due to the fewer number of patients who had recent LIC assessments. Of interest, patients with NTDT reported better QoL at younger age compared to TDT patients but there was a trend toward worse QoL at older age. Our results provide a better understanding of the factors that affect the QoL of thalassemia patients and highlights the importance of management of body iron in both TDT and specially in NTDT patients. In addition, it confirms the notion that while NTDT patients may not require regular transfusions based on conventional criteria, they may experience significant reduction in QoL especially at older ages. Further efforts to address the health and QoL of NTDT patients are required to improve the outcomes of this often neglected condition. (Funded by a research grant from the Thalassemia Foundation of Canada) Disclosures Taher: Celgene: Research Funding; Novartis: Honoraria, Research Funding.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.424

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.027
GPT teacher head0.293
Teacher spread0.266 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations24
Published2016
Admission routes2
Has abstractyes

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