The Outcome of Using Closed Suction Wound Drains in Patients Undergoing Lumbar Spine Surgery: A Systematic Review
Bibliographic record
Abstract
Study Design Systematic review. Objective Determine whether closed suction wound drains decrease the incidence of postoperative complications compared with no drain use in patients undergoing spine surgery for lumbar degenerative conditions. Methods Electronic databases and reference lists of key articles were searched up through January 22, 2015, to identify studies comparing the use of closed suction wound drains with no drains in spine surgery for lumbar degenerative conditions. Outcomes assessed included the cumulative incidence of epidural hematoma, superficial and deep wound infection, and postoperative blood transfusion. The overall strength of evidence across studies was based on precepts outlined by the Grades of Recommendation Assessment, Development and Evaluation Working Group. Results Five heterogeneous studies, three randomized controlled trials, and two cohort studies form the evidence basis for this report. There was no difference in the incidence of hematoma, superficial wound infection, or deep infection in patients with compared with patients without closed suction wound drains after lumbar surgery. The upper bounds of the 95% confidence interval for hematoma ranged from 1.1 to 16.7%; for superficial infection, 1.0 to 7.3%; and for deep infection, 1.0 to 7.1%. One observational study reported a 3.5-fold increase in the risk of blood transfusion in patients with a drain. The overall strength of evidence for these findings is considered low or insufficient. Conclusions Conclusions from this systematic review are limited by the quality of included studies that assessed the use of closed suction wound drains in lumbar spine surgeries for degenerative conditions. We believe that spine surgeons should not routinely rely on closed suction wound drains in lumbar spine surgery until a higher level of evidence becomes available to support its use.
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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 | 0.011 | 0.061 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 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".