MétaCan
Menu
Back to cohort
Record W2770386997 · doi:10.1136/jclinpath-2017-204724

Frequency of and reasons for paroxysmal nocturnal haemoglobinuria screening in patients with unexplained anaemia

2017· article· en· W2770386997 on OpenAlexaff
James T. England, Bakul I. Dalal, Heather A. Leitch

Bibliographic record

VenueJournal of Clinical Pathology · 2017
Typearticle
Languageen
FieldImmunology and Microbiology
TopicComplement system in diseases
Canadian institutionsVancouver General HospitalSt. Paul's HospitalUniversity of British Columbia
Fundersnot available
KeywordsMedicineParoxysmal nocturnal hemoglobinuriaPediatricsBioinformaticsInternal medicineImmunologyBiology

Abstract

fetched live from OpenAlex

Referral to hematology for anemia is common. In paroxysmal nocturnal hemoglobinuria (PNH), cells deficient in the glycosylphosphatidyl inositol (GPI) anchor are lysed by complement. Eculizumab improves overall survival and quality of life while reducing hemolysis, transfusion requirements, and thrombosis. We evaluated the frequency of screening for PNH in patients with unexplained anemia. Key clinical features, laboratory data, and investigations were recorded for patients referred for anemia since 2010, without a specific cause found. PNH testing was done by flow cytometry. 540 patients had: anemia not yet diagnosed (NYD, n=318 (including unexplained iron deficiency, n=92; DAT-negative hemolysis, n=9)); anemia of chronic disease, n=173; and pancytopenia NYD, n=49. 82.4% had LDH testing done; 85.0% total bilirubin; 78.7% reticulocyte counts; and 40.6% haptoglobin level; 131 (24.2%) had possible hemolysis. PNH testing was done in 56 (10.4%). Those screened for PNH were more likely to have: younger age (P=0.04); a history of thrombosis (P<0.001); undergone a BMBx (P<0.001); received RBC transfusions (P=0.0018); or evidence of DAT-negative hemolysis (P<0.001). In summary, PNH was tested for in a minority of patients with unexplained anemia (10.4%) despite potential indicators of hemolysis in 24.2%. Increased screening could identify patients who would benefit from treatment and should be considered.

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)0.003
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 score0.010
Threshold uncertainty score0.407

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
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 integrity0.0000.000
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 head0.058
GPT teacher head0.374
Teacher spread0.315 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Clinical PathologySame topicComplement system in diseasesFrench-language works237,207