Is blood transfusion associated with an increased risk of infection among spine surgery patients?
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.011 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".