MétaCan
Menu
Back to cohort
Record W2803157865 · doi:10.1111/bjh.15274

How i treat primary haemophagocytic lymphohistiocytosis

2018· review· en· W2803157865 on OpenAlexaff
Rebecca Marsh, Élie Haddad

Bibliographic record

VenueBritish Journal of Haematology · 2018
Typereview
Languageen
FieldMedicine
TopicAutoimmune and Inflammatory Disorders Research
Canadian institutionsUniversité de MontréalCentre Hospitalier Universitaire Sainte-Justine
Fundersnot available
KeywordsMedicineIntensive care medicineDiseaseHemophagocytic lymphohistiocytosisTransplantationHematopoietic stem cell transplantationPediatricsImmunologyPathologyInternal medicine

Abstract

fetched live from OpenAlex

Primary haemophagocytic lymphohistiocytosis (HLH) diseases are a collection of inherited genetic disorders that cause the syndrome of HLH. Great advances have been made in the last 20 years with regard to the discovery of many of the genetic aetiologies of disease. Several advances have also been made on the clinical stage. Accurate screening diagnostics for primary HLH diseases that are superior to traditional Natural Killer cell function testing have been developed and are now available in many countries. There is now grounded clinical experience on which to base routine treatment decisions for patients with HLH. Newer approaches to allogeneic haematopoietic cell transplantation have increased overall patient survival. Despite these advances, however, there is still much work to be done to further improve patient care. This 'How I Treat' article will focus on summarizing current diagnostic, treatment and transplant strategies for patients with primary HLH diseases.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0050.004

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 head0.049
GPT teacher head0.333
Teacher spread0.284 · 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

Citations61
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueBritish Journal of HaematologySame topicAutoimmune and Inflammatory Disorders ResearchFrench-language works237,207