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2003· article· en· W4255359149 on OpenAlexaff
Nancy M. Heddle

Bibliographic record

VenueTransfusion · 2003
Typearticle
Languageen
FieldMedicine
TopicBlood transfusion and management
Canadian institutionsMcMaster University
Fundersnot available
KeywordsPlateletImmunologyMedicineChemistry

Abstract

fetched live from OpenAlex

The above letter was sent to Heddle et al.; Ms Heddle offered the following reply. We thank Dr Blumburg and colleagues for their letter and for bringing to our attention their publications related to CD40 ligand. We agree with Dr Blumburg that reactions to platelet concentrates have a multifactorial etiology. Indeed, we have suggested in several previous publications that both the cellular and the plasma components contribute to these reactions and that the plasma supernatant represents a “soup” of biologic response modifiers that may be derived from the cellular components within the product or noncellular interactions.1-3 The soluble biologic response modifiers in the plasma that may be contributing to these reactions have been the subject of many investigations in several different countries. The initial focus was that these biologic response modifiers were WBC-derived as studies showed that WBC reduction before storage could decrease the frequency of these reactions. However, as noted in our study, there is still an overall frequency of acute febrile transfusion reactions of approximately 6 to 7 percent even when platelets that are WBC-reduced before are used. Dr Blumburg's suggestion that biologic response modifiers derived from platelets are involved in these reactions is logical, and indeed their publication suggests an association between CD40 ligand and reactions.4 There is no doubt that the plasma supernatant in stored platelet products is a soup of biologic response modifiers derived from a number of different sources, and the roles of these different components in causing reactions is not clearly understood. It is possible that the exact mechanism that causes these reactions may never be clearly defined because it is likely that these reactions represent a complex interaction between various cytokines, other biologic response modifiers, and complex clinical factors. We also agree with Dr Blumburg's suggestion that plasma removal in addition to WBC reduction may be an effective way to prevent subsequent reactions in patients who received WBC-reduced products. Their study published in Transfusion Medicine supports the concept that residual plasma removed through centrifugation or washing may be an effective mechanism for preventing these reactions.5 In 1996, we suggested a stepwise approach to treat patients who had recurrent febrile transfusion reactions, and indeed a combination of plasma removal and WBC reduction was recommended.6 For approximately 4 years our institution has used this combined intervention for patients who have repeated reactions to platelet concentrates that have been WBC-reduced before storage. Our approach was not to wash the platelets, but to simply remove the plasma supernatant from the platelet product, and I am not aware of any patient who has continued to react when this approach was used. Hence, the suggestion that plasma removal may be as effective as washing the platelets is probably correct; however, it would be useful to see some further data to confirm the effectiveness of this approach.

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)0.028
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.955
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.028
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0030.002
Scholarly communication0.0030.004
Open science0.0020.002
Research integrity0.0270.024
Insufficient payload (model declined to judge)0.0450.036

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 head0.016
GPT teacher head0.253
Teacher spread0.237 · 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.

Study designNot applicable
Domainnot available
GenreOther

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
Published2003
Admission routes1
Has abstractyes

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