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Record W2948965931 · doi:10.1055/s-0039-1684031

First-Line Therapy for Immune Thrombocytopenia

2019· review· en· W2948965931 on OpenAlexaff
Siraj Mithoowani, Donald M. Arnold

Bibliographic record

VenueHämostaseologie · 2019
Typereview
Languageen
FieldMedicine
TopicPlatelet Disorders and Treatments
Canadian institutionsCanadian Blood ServicesMcMaster University
Fundersnot available
KeywordsImmune thrombocytopeniaMedicinePrednisonePlateletImmune systemFirst line therapyImmunologyIntensive care medicineInternal medicine

Abstract

fetched live from OpenAlex

Immune thrombocytopenia (ITP) is an autoimmune disease affecting blood platelets that causes thrombocytopenia and an increased risk of bleeding. First-line therapy is indicated for patients with bleeding complications or who are at increased risk of bleeding, and the decision to initiate therapy depends not only on the platelet count, but also on other endpoints including quality of life. The choice of first-line therapy depends primarily on how quickly a platelet count response is required, with intravenous immune globulin providing the more rapid response, followed by high-dose dexamethasone and prednisone. In this narrative review, we discuss key issues with first-line therapy in ITP including when to initiate therapy, treatment options and special considerations for children. Evidence-based guidelines are lacking for the emergency management of patients with ITP who present with significant bleeding; we provide our approach to this critical situation.

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 score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.002

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 head0.203
GPT teacher head0.416
Teacher spread0.213 · 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

Citations42
Published2019
Admission routes1
Has abstractyes

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