RETRACTED: The impact of negative pressure wound therapy on surgical wound infection, hospital stay and postoperative complications after spinal surgery: A meta‐analysis
Post-publication record
Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement; it reports them as false, which reads as “fine”.
Bibliographic record
Abstract
To systematically assess the effect of negative pressure wound therapy (NPWT) on postoperative surgical wound infection, length of hospital stay and postoperative complications after spinal surgery. Relevant studies on the application of NPWT in spinal surgery were conducted via a computerised database search, including PubMed, EMBASE, Web of Science, MEDLINE, Cochrane Library, China National Knowledge Infrastructure (CNKI) and Wanfang, from inception to June 2023. The identified literature was rigorously screened and data extraction was performed by two investigators independently. The quality of the relevant studies was evaluated using the Newcastle-Ottawa scale (NOS). The effect size for count data was determined by the odds ratio (OR), while the impact size for measurement data was expressed as the standardised mean difference (SMD). The 95% confidence interval (CI) was calculated for each effect magnitude. Stata 17.0 software was used for the meta-analysis. Ten papers, totalling 1448 patients, were finally included. This study demonstrated that NPWT led to a statistically significant reduction in the occurrence of postoperative surgical wound infections (OR: 0.377, 95% CI: 0.238-0.598, p < 0.001), fewer postoperative complications (OR: 0.526, 95% CI: 0.360-0.770, p = 0.001) and a shortened hospital stay (SMD: -0.678, 95%CI: -1.324 to -0.031, p = 0.040) after spinal surgery compared with the control group. When compared with other treatment approaches, NPWT also demonstrated a substantial reduction in surgical wound infections and postoperative complications, as well as a shorter duration of hospitalisation after spinal 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 | 0.020 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.059 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".