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Record W2550526019 · doi:10.1111/voxs.12310

Investigation and management of non‐infectious transfusion reactions

2016· article· en· W2550526019 on OpenAlexaff
Gwen Clarke

Bibliographic record

VenueISBT Science Series · 2016
Typearticle
Languageen
FieldMedicine
TopicBlood transfusion and management
Canadian institutionsCanadian Blood Services
Fundersnot available
KeywordsMedicineAdverse effectIntensive care medicineRashVital signsBlood transfusionEmergency medicineAnesthesiaSurgeryInternal medicine

Abstract

fetched live from OpenAlex

Appropriate management of non‐infectious adverse transfusion reactions begins with recognition that a change in clinical status during or following a transfusion may represent an adverse event. Appropriate monitoring of patients during transfusion and explicit training of staff to recognize the signs and symptoms of adverse transfusion reactions is the key to diagnosis and to management. As some reactions may occur in the hours following transfusion, patient education and instruction on reporting relevant symptoms are also important. The typical symptoms that herald the onset of a transfusion‐related adverse event include fever, rash, shock and respiratory distress. Haemoglobinuria may also be a presenting feature. Early signs or symptoms may reflect more than one type of reaction. All transfusionists must be aware of the steps in acute management of a suspected adverse transfusion reaction. For those events that occur while the transfusion is ongoing, stopping the infusion and maintaining the intravenous access are the important first step. Rapid evaluation of the patient's vital signs, a bedside check of the unit and patient identification, as well as assessment of the appearance of the blood component, are early steps. Supportive care based on the patient's signs and symptoms must occur while additional laboratory and clinical investigations are initiated. In most cases of transfusion‐related adverse events, a ‘posttransfusion’ blood sample should be evaluated for the possibility of serological incompatibility. In addition, most moderate and severe reactions would be investigated with a blood count, renal and liver function tests and assessment of urine for haemoglobin. Other specific investigations depend on the presenting features and initial serologic findings. Based on the clinical, laboratory and/or imaging studies, most transfusion‐related adverse events can be classified into one of the categories of acute transfusion reactions. These include acute haemolytic transfusion reactions, febrile non‐haemolytic, allergic, anaphylactoid, septic, circulatory overload, hypotension and transfusion‐related acute lung injury. Transfusion‐associated graft‐versus‐host disease and posttransfusion purpura can be considered in some circumstances and delayed haemolytic transfusion reactions may be seen in the days to week following a transfusion. This diagnostic classification is important in optimizing acute management and may also contribute to decisions about component selection for subsequent transfusion. Reporting of adverse transfusion events is also an important part of management. Reporting to the hospital blood bank assists with diagnosis and decisions regarding future blood component therapy. The hospital transfusion committee may monitor transfusion reaction rates and trends as a quality indicator that can be used to change practices. The blood supplier must be notified of all reactions which may be attributable to a particular donor or donor unit, especially if recall or quarantine of associated blood components may be necessary. Regional or national haemovigilance programmes may require notification and can contribute to changes in standard practices to address common or serious adverse transfusion events and in early recognition of uncommon complications.

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)0.002
metaresearch head score (Gemma)0.006
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: none
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.003

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 head0.014
GPT teacher head0.253
Teacher spread0.239 · 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

Citations2
Published2016
Admission routes1
Has abstractyes

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