Canadian Consensus for the Prevention of Blood Loss in Spine Surgery
Bibliographic record
Abstract
STUDY DESIGN: Cross-sectional, modified Delphi approach. OBJECTIVE: The primary objective of this study was to identify patients at risk of increased perioperative blood loss according to the opinion of expert spine surgeons across Canada. The secondary objective was to obtain information about the experts' approach on how to minimize significant blood loss perioperatively. SUMMARY OF BACKGROUND DATA: Significant blood loss in major spinal surgeries has been associated with increased intra- and perioperative complications and costs. The current available evidence regarding risk factors and preventive measures for increased blood loss remains incomplete. METHODS: A modified Delphi approach was employed to generate consensus opinion on the risk factors and preventive measures for significant blood loss in major spinal surgeries. Twenty-five spine surgeons in Canada participated in this study. RESULTS: Among various factors, surgery for the treatment of spine tumors and prolonged operative time of greater than 5 hours were found to be the most important predictive factors for blood loss in spine surgery. On the other hand, appropriate surgical hemostasis was considered the most effective measure for the prevention of blood loss in these surgeries. CONCLUSION: We recommend the reduction of blood loss by means of meticulous hemostasis and shorter operative time when it is safe and possible. This might result in better treatment outcomes. It would also lead to a reduction in costs associated with major spine surgeries and would ultimately lead to greater value-based spine care. LEVEL OF EVIDENCE: 4.
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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 | 0.051 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".