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Transfusion practice in elective orthopaedic surgery

2001· article· en· W2040733240 on OpenAlexaffabout
Brian G. Feagan, Cindy J. Wong, Catherine Y. Lau, Susan Wheeler, Greg Sue-A-Quan, Alexandra Kirkley

Bibliographic record

VenueTransfusion Medicine · 2001
Typearticle
Languageen
FieldMedicine
TopicBlood transfusion and management
Canadian institutionsRobarts Clinical TrialsWestern University
Fundersnot available
KeywordsMedicineConfidence intervalRheumatoid arthritisBlood transfusionArthroplastyOrthopedic surgerySurgeryInternal medicine

Abstract

fetched live from OpenAlex

. The transfusion requirements of 2233 patients who underwent total hip or knee joint arthroplasty procedures at nine Canadian hospitals during 1995-1996 were evaluated. Although 64% of patients were eligible for participation in an autologous blood donation (ABD) programme, only 8% predonated blood. Patients who were eligible for ABD were younger (62 years vs. 70 years) and had fewer medical illnesses (18% vs. 44%) than those who did not predonate. The rate of allogeneic transfusion was 9.0% (95% confidence interval 4.9-13.1%) in patients who predonated as compared with 24.1% (95% confidence interval 22.2-25.9%) in those who did not. Risk factors for the occurrence of an allogeneic transfusion were type of procedure (primary or revision hip arthroplasty), lower baseline haemoglobin, lower body weight, older age and presence of rheumatoid arthritis (P < 0.001). Only patients without risk factors were predicted to have a less than 10% risk of receiving an allogeneic transfusion. Use of preventive strategies was minimal. Two models designed to predict the occurrence of an allogeneic transfusion were evaluated. If allogeneic transfusion rates are to be reduced, eligible patients should be encouraged to participate in ABD programmes. For patients who are ineligible, other preventative strategies should be introduced.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.744
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.021
GPT teacher head0.297
Teacher spread0.276 · 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.

Study designOther design
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

Citations79
Published2001
Admission routes2
Has abstractyes

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