P.132 Blood loss quantification and management strategies in cranial neurosurgery: a systematic review
Bibliographic record
Abstract
Background: Blood loss quantification and management are important facets of cranial surgery, having been linked with adverse outcomes if management is inadequate. While many studies report estimated blood loss (EBL) as an outcome measure, inconsistencies exist in EBL quantification and management strategies Methods: A systematic review of cranial surgery literature on blood loss measurement and management was conducted according to PRISMA guidelines utilizing a novel software platform, Nested Knowledge Results: Initial search yielded 1029 non-duplicated. 107 full-text studies were included. 70% of studies were retrospective. Most common treatment conditions were 41% craniosynostosis (44/107) and 36% tumor (39/107). Most common EBL measurement methods were comparison of pre-operative and post-operative hemoglobin/hematocrit in 46.7% (50/107), anesthesia record in 26.2% (28/107), and surgeon estimation in 9.3% (10/107). 53.3% of studies did not specify a quantification methodology. Blood loss management strategies also varied, with transfusion being the most common method in 64.5% (69/107) of studies Conclusions: EBL quantification and blood loss management remain important clinical and research metrics. Despite this, significant heterogeneity exists in blood loss quantification and management strategies, with most studies providing no data on EBL quantification. Standardization of EBL quantification/reporting should be undertaken to improve comparability and consistency across studies.
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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 | 0.013 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".