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Record W1549872517 · doi:10.1111/ijlh.12347

Epidemiology and diagnostic testing for hemochromatosis and iron overload

2015· review· en· W1549872517 on OpenAlexaff
Paul C. Adams

Bibliographic record

VenueInternational Journal of Laboratory Hematology · 2015
Typereview
Languageen
FieldMedicine
TopicIron Metabolism and Disorders
Canadian institutionsWestern University
FundersNational Heart and Lung Institute
KeywordsHemochromatosisEpidemiologyHereditary hemochromatosisMedicineIntensive care medicinePathologyInternal medicine

Abstract

fetched live from OpenAlex

Hemochromatosis is the most common genetic disease in northern European populations. Body iron stores progressively increase in most patients, which can lead to cirrhosis of the liver, hepatocellular carcinoma, heart failure, arthritis, and pigmentation. Simple blood tests such as the serum ferritin and transferrin saturation are useful to suggest the diagnosis which can be confirmed in most cases with a simple genetic test for the C282Y mutation of the HFE gene. However, these blood tests are often misinterpreted and there are rare patients with iron overload without HFE mutations. A diagnostic approach is presented based on a large referral practice and a population-based study (HEIRS) which screened for iron overload in 101,168 participants.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.003
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.077
GPT teacher head0.406
Teacher spread0.329 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations114
Published2015
Admission routes1
Has abstractyes

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