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Thalassaemia in children: from quality of care to quality of life

2015· review· en· W1962043507 on OpenAlexafffund
Ali Amid, Antoine N. Saliba, Alì Taher, Robert J. Klaassen

Bibliographic record

VenueArchives of Disease in Childhood · 2015
Typereview
Languageen
FieldMedicine
TopicHemoglobinopathies and Related Disorders
Canadian institutionsChildren's Hospital of Eastern OntarioSickKids FoundationUniversity of OttawaHospital for Sick ChildrenUniversity of Toronto
FundersNovartis Pharmaceuticals CanadaThalassemia Foundation of CanadaIC Design Education CenterCelgeneBiogen
KeywordsMedicineIntensive care medicineQuality of life (healthcare)Multidisciplinary approachDiseaseDeveloped countryThalassemiaDeveloping countryPediatricsEnvironmental healthNursingPopulationEconomic growthPathologyInternal medicine

Abstract

fetched live from OpenAlex

Over the past few decades, there has been a remarkable improvement in the survival of patients with thalassaemia in developed countries. Availability of safe blood transfusions, effective and accessible iron chelating medications, the introduction of new and non-invasive methods of tissue iron assessment and other advances in multidisciplinary care of thalassaemia patients have all contributed to better outcomes. This, however, may not be true for patients who are born in countries where the resources are limited. Unfortunately, transfusion-transmitted infections are still major concerns in these countries where paradoxically thalassaemia is most common. Moreover, oral iron chelators and MRI for monitoring of iron status may not be widely accessible or affordable, which may result in poor compliance and suboptimal iron chelation. All of these limitations will lead to reduced survival and increased thalassaemia-related complications and subsequently will affect the patient's quality of life. In countries with limited resources, together with improvement of clinical care, strategies to control the disease burden, such as public education, screening programmes and appropriate counselling, should be put in place. Much can be done to improve the situation by developing partnerships between developed countries and those with limited resources. Future research should also particularly focus on patient's quality of life as an important outcome of care.

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 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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.521
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.032
GPT teacher head0.351
Teacher spread0.319 · 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.

Study designObservational
Domainnot available
GenreReview

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

Citations30
Published2015
Admission routes2
Has abstractyes

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