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Interpretation and inspiration of Guidance on Platelet Transfusion for Patients with Hypoproliferative Thrombocytopenia

2016· article· en· W3029486414 on OpenAlexaboutno aff
Jing lJing, Xingbin Hu, Yan Chang, Yizhen Ge, Qingping Zhang

Bibliographic record

VenueGuoji shuxue ji xueyexue zazhi · 2016
Typearticle
Languageen
FieldMedicine
TopicPlatelet Disorders and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsApheresisMedicinePlatelet transfusionPlateletBlood transfusionTransfusion medicineIntensive care medicineInternal medicine

Abstract

fetched live from OpenAlex

Guidance on Platelet Transfusion for Patients with Hypoproliferative Thrombocytopenia(hereinafter referred to as the Guide)was created by an international professional organization which composed of 24 national experts in blood transfusion from Britain, the United States, Canada, Germany, Belgium and Australia, in order to standardize the diagnosis and treatment of hypoproliferative thrombocytopenia.They updated it in 2015.The author interpret the content of the Guide in seven respects: the need for prophylactic platelet transfusions, minimum platelet threshold, platelet transfusion dose, transfusion with same blood type, transfusion of RhD negative patients with RhD positive platelet, platelet cross matching test and comparison of the efficacy between apheresis platelets and concentrated plasma.Meanwhile the the status of the diagnosis and treatment of the disease are compared between at home and abroad.The Guide can provide a theoretical basis for clinical diagnosis and treatment of the disease. Key words: Thrombocytopenia; Platelet transfusion; Guidebooks

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)0.003
metaresearch head score (Gemma)0.022
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: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.009
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.022
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0090.008

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 head0.010
GPT teacher head0.250
Teacher spread0.240 · 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
GenreCommentary

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
Published2016
Admission routes1
Has abstractyes

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