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Record W2096958450 · doi:10.1111/imj.12768

Conditions associated with extreme hyperferritinaemia (&gt;3000 μg/<scp>L</scp>) in adults

2015· article· en· W2096958450 on OpenAlexaffabout
Andrew Wormsbecker, David Sweet, S. L. Mann, Sophie Wang, Morris Pudek, Luke Y. C. Chen

Bibliographic record

VenueInternal Medicine Journal · 2015
Typearticle
Languageen
FieldMedicine
TopicAutoimmune and Inflammatory Disorders Research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineFerritinSerum ferritinInternal medicineDifferential diagnosisGastroenterologyDiseasePediatricsPathology

Abstract

fetched live from OpenAlex

BACKGROUND: The clinical significance of extreme hyperferritinaemia has come under scrutiny with the increasing recognition of haemophagocytic lymphohistiocytosis (HLH) in adults. Most studies of hyperferritinaemia have focused on serum ferritin greater than 1000 μg/L, often in ambulatory patients. The conditions associated with more extreme hyperferritinaemia are poorly understood. AIMS: To examine conditions associated with extreme hyperferritinaemia greater than 3000 μg/L in acutely ill adults at a quaternary care hospital. METHODS: Patients with serum ferritin greater than 3000 μg/L at Vancouver General Hospital between 1 August 2011 and 1 August 2012 were identified. Those over 18 years of age and with clinical data available were included in the study. RESULTS: Eighty-three subjects were identified. Twenty-one cases (25%) were due to transfusional iron overload, 16 (19%) due to liver disease and 15 (18%) due to mixed factors. Haemophagocytic lymphohistiocytosis (HLH) was diagnosed in six of 83 patients (7%) with ferritin greater than 3000 μg/L, but six of eight patients (75%) with ferritin greater than 20 000 μg/L. CONCLUSIONS: Extreme hyperferritinaemia greater than 3000 μg/L is uncommon in adult patients. The highest serum ferritin values are seen in HLH, but the differential diagnosis for serum ferritin greater than 3000 μg/L remains broad with iron overload and liver disease being the most common causes.

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)0.001
metaresearch head score (Gemma)0.005
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 score0.282
Threshold uncertainty score0.900

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.044
GPT teacher head0.312
Teacher spread0.268 · 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

Citations29
Published2015
Admission routes2
Has abstractyes

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