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Record W4404216838 · doi:10.21037/aob-24-21

A dose of platelets: getting it just right

2024· article· en· W4404216838 on OpenAlexaboutno aff
Mark T. Friedman, Behnam Rafiee, Timothy Hilbert, Mansab Jafri, Ding Wen Wu

Bibliographic record

VenueAnnals of Blood · 2024
Typearticle
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsnot available
Fundersnot available
KeywordsPlateletMedicineInternal medicine

Abstract

fetched live from OpenAlex

Abstract: Platelet transfusions are commonly administered to patients with thrombocytopenia, hemorrhage and platelet dysfunction. Although the optimal threshold for transfusion and the appropriate number of platelets per dose are not defined in all clinical situations, a platelet count of 10,000/µL is a generally accepted threshold for prophylactic transfusions in uncomplicated patients. Meanwhile, the minimum yield for collection or the size of a standard dose is higher in the United States under the Food and Drug Administration regulatory requirements than in European nations and in Canada. Given the challenges in maintaining donor recruitment and product supplies, minimal yield specifications have come under scrutiny, and utilization criteria for platelet products have come under investigation with evidence that many prophylactic platelet transfusions are administered to patients when their counts are above the recommended guideline threshold without evidence of bleeding. These findings suggest that there are opportunities to improve practices surrounding platelet collections and transfusion practices, reducing platelet dosages and number of transfusions administered, that would positively affect the availability of platelet products in the face of severe supply shortages and avoid platelet transfusions that provide little to no significant clinical benefit to patients and potentially cause more harm than good.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.505
Threshold uncertainty score0.480

Codex and Gemma teacher scores by category

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

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 head0.075
GPT teacher head0.361
Teacher spread0.286 · 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 designNot applicable
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

Citations1
Published2024
Admission routes1
Has abstractyes

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