Modifiable risk factors for perioperative hidden blood loss in unilateral biportal endoscopic surgery: a systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Unilateral biportal endoscopic (UBE) surgery enables precise treatment of lumbar spine pathologies due to its inherent advantages typical of minimally‑invasive endoscopic procedures, including reduced intraoperative blood loss and minimal soft tissue dissection. However, hidden blood loss (HBL) remains a significant challenge in UBE, with limited data regarding its incidence and risk factors. AIM: This study aimed to investigate risk factors associated with HBL in UBE surgery. MATERIALS AND METHODS: Original studies evaluating risk factors for HBL in UBE surgery were systematically searched in MEDLINE, Embase, China National Knowledge Infrastructure, Wanfang Data, and the Cochrane Central Register of Controlled Trials (up to March 2025). The included studies met the quality assessment criteria of the Newcastle‑Ottawa Scale. RESULT: Six studies involving 601 patients subjected to lumbar UBE surgery were included. Our meta‑analysis identified that higher body mass index (BMI), prolonged surgical time, preoperative hypertension, and elevated preoperative hematocrit (HCT) levels were significant risk factors for increased HBL in UBE surgery (P <0.05). Sensitivity analysis confirmed the robustness of these findings, with no changes in the significance of the pooled results. CONCLUSION: Higher BMI, prolonged surgical time, preoperative hypertension, and elevated preoperative HCT levels are associated with an increased risk of HBL in patients undergoing lumbar UBE surgery. This study serves as a baseline reference for developing public health strategies to mitigate HBL in UBE procedures.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.032 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".