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Record W2960633148 · doi:10.1097/md.0000000000016287

Is blood transfusion associated with an increased risk of infection among spine surgery patients?

2019· review· en· W2960633148 on OpenAlexaboutno aff
Yukun He, Huizi Li, Huading Lu

Bibliographic record

VenueMedicine · 2019
Typereview
Languageen
FieldMedicine
TopicBlood transfusion and management
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePerioperativeBlood transfusionOdds ratioMeta-analysisCochrane LibraryCohort studyMEDLINECohortSurgeryInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Blood transfusions are associated with many adverse outcomes among spine surgery patients, but it remains unclear whether perioperative blood transfusion during spine surgery and postoperative infection are related. Recently, many related cohort studies have been published on this topic. METHODS: This study was performed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. The PubMed, Embase, and Cochrane Library databases were searched for eligible published studies. The Newcastle-Ottawa Scale (NOS) was used to assess the methodological quality of the studies, and a random-effects model was used to calculate the odds ratios (ORs) with 95% CIs. Sensitivity analyses were conducted to explore the source of heterogeneity. RESULTS: The final analysis included 8 cohort studies with a total of 34,185 spine surgery patients. These studies were considered to be of high or moderate quality based on their NOS scores, which ranged from 5 to 9. Pooled estimates indicated that blood transfusion increased the infection rate (OR, 2.99; 95% CI, 1.95 to 4.59; I = 86%), which was consistent with the sensitivity analyses. CONCLUSIONS: Our results suggest that perioperative blood transfusion is a risk factor for postoperative infection among spine surgery patients. Further study is necessary to identify other influencing factors and to establish the mechanism underlying this relationship. Additional measures may be needed to reduce unnecessary blood transfusions during spine surgery.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.749
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.035
GPT teacher head0.290
Teacher spread0.256 · 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
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

Citations39
Published2019
Admission routes1
Has abstractyes

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