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Record W2079728504 · doi:10.1002/lt.20798

Reply: Reduction of blood product transfusion requirements during liver transplantation

2006· article· en· W2079728504 on OpenAlexaffabout
Luc Massicotte, Serge Lénis, Lynda Thibeault, Marie‐Pascale Sassine, Ruth L. Seal, André Roy

Bibliographic record

VenueLiver Transplantation · 2006
Typearticle
Languageen
FieldMedicine
TopicLiver Disease and Transplantation
Canadian institutionsUniversity of AlbertaHôpital Saint-Luc
Fundersnot available
KeywordsPhlebotomyMedicineLiver transplantationBlood productBlood transfusionOdds ratioCentral venous pressureSurgeryAnesthesiaIntensive care medicineTransplantationInternal medicineBlood pressureHeart rate

Abstract

fetched live from OpenAlex

We thank Dr. E. Pivalizza et al. for their interest in our work. Only 44.5% of our patients underwent phlebotomy, and all patients in both series had aprotinin according to the Hammersmith protocol.1 Phlebotomy was one of the 3 variables linked to no red blood cell (RBC) transfusion during orthotopic liver transplantation (OLT) from logistic regression. We gave the odds ratio from the 304 patients studied; when we performed phlebotomy, the risk of transfusing RBC decreased from 79% to 13%. Moreover, we did not conclude that there was a causal relationship between phlebotomy and no RBC transfusions. Phlebotomy is a tool to decrease the central venous pressure (CVP), but it is not an end in itself. We believe that maintaining a low CVP is the most important clinical factor that permits OLT without transfusion. Regardless of which technique is used (phlebotomy or fluid restriction), it is the lowering of the CVP during liver dissection, before the anhepatic phase, that contributes to limit blood loss and hence leads to a decreased transfusion rate. Phlebotomy results in a faster decrease of the CVP when compared with fluid restriction. The purpose of our study was not to compare the level of morbidity between our patients and patients from other centers. We wanted to evaluate ways to transfuse less blood products in patients with comparable disease severity. Our patients, when compared with patients in other series, seem to be as sick, if not sicker. The patients of Frasco et al.,2 with a RBC transfusion rate of 2.9 ± 2.7 units per patient, had the same severity of disease for their patients as ours. The patients of Ramos et al.,3 who had the same transfusion rate for RBC, had a starting international normalized ratio of 1.2 ± 0.2. What is more, 40% of the patients in the last series received a diagnosis of hepatocellular carcinoma as a result of hepatitis C, vs. 7% in our series. A total of 73% of their patients came from their home. Finally, in our series, 13 patients had undergone a previous OLT, and some had undergone OLT and a renal transplant. We can add that after more than 200 OLTs performed at our center using this strategy, the transfusion rate is still the same. With this transfusion rate, it is very difficult to study strategies aimed at decreasing blood loss or transfusion rate. We are limited to observational studies or historical controls. We hope that other liver transplantation centers would undertake the study of these new concepts of not correcting coagulation defects, lowering CVP, or performing phlebotomy during OLT. Dr. Luc Massicotte*, Dr. Serge Lénis*, Dr. Lynda Thibeault , Dr. Marie-Pascale Sassine , Dr. Robert F. Seal?, Dr. Andre Roy?, * Department of Anesthesiology, Hôpital St-Luc (CHUM), Montreal, Quebec, Canada, Department of Epidemiology, Hôpital St-Luc (CHUM), Montreal, Quebec, Canada, Department of Biostatistics, Hôpital St-Luc (CHUM), Montreal, Quebec, Canada, ? Department of Anesthesiology, University of Alberta, Edmonton, Alberta, Canada, ? Department of Surgery, Hôpital St-Luc (CHUM), Montreal, Quebec, Canada

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)0.004
metaresearch head score (Gemma)0.037
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 score0.019
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.037
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0020.001
Research integrity0.0190.018
Insufficient payload (model declined to judge)0.0040.004

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 head0.013
GPT teacher head0.236
Teacher spread0.223 · 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".

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Citations0
Published2006
Admission routes2
Has abstractyes

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