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Record W2018012230 · doi:10.1309/p6hm33f4d05t30ym

Prevalence of Thalassemia in Patients With Microcytosis Referred for Hemoglobinopathy Investigation in Ontario

2007· article· en· W2018012230 on OpenAlexafffundabout
John Lafferty, David Barth, B. Sheridan, Andrew McFarlane, Linda M. Halchuk, Mark Crowther

Bibliographic record

VenueAmerican Journal of Clinical Pathology · 2007
Typearticle
Languageen
FieldMedicine
TopicHemoglobinopathies and Related Disorders
Canadian institutionsUniversity of TorontoMcMaster UniversitySt. Joseph’s Healthcare HamiltonHamilton Regional Laboratory Medicine Program
FundersMcMaster University
KeywordsMicrocytosisHemoglobinopathyThalassemiaMedicineHydrops fetalisHemoglobin A2Beta thalassemiaAlpha-thalassemiaPediatricsAnemiaInternal medicineHemolytic anemiaIron deficiencyGeneticsGenotypeFetusPregnancyBiology

Abstract

fetched live from OpenAlex

In Ontario, Canada, beta-thalassemia is easily detected through measurement of hemoglobin A2, but most laboratories do not do exhaustive DNA investigations for alpha-thalassemia. Therefore, the prevalence of thalassemia in microcytic samples for hemoglobinopathy investigation in Ontario is unknown. To address this, we performed a prospective cohort study in which samples referred for hemoglobinopathy investigation were also evaluated for alpha-thalassemia by DNA testing. Of 800 samples submitted, 664 were evaluable. Of the 664 patients represented, 163 (24.5%) were beta-thalassemia major carriers, 68 (10.2%) were hemoglobin Bart's hydrops fetalis carriers and, in total, 361 (54.4%) had some form of thalassemia. We conclude that microcytosis due to thalassemia is common in Ontario and that major forms of thalassemia, including forms predisposing to hemoglobin Bart's hydrops fetalis and beta-thalassemia major, are frequent. This illustrates the importance of adequate prenatal and laboratory investigation for these abnormalities in Ontario and other similar multiethnic jurisdictions worldwide.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.016
Threshold uncertainty score0.399

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.022
GPT teacher head0.318
Teacher spread0.296 · 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

Citations12
Published2007
Admission routes3
Has abstractyes

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