Comparison of the Loop Technique With Incision and Drainage for Skin and Soft Tissue Abscesses: A Systematic Review and Meta‐analysis
Bibliographic record
Abstract
BACKGROUND: Cutaneous abscesses are common presentations to the emergency department. While the primary treatment for most abscesses is conventional incision and drainage (CID), this is painful and can lead to multiple return visits. The loop drainage technique (LDT) has been proposed as an alternate, less-invasive approach to abscess management. The primary outcome of this study was to compare LDT with CID for skin and soft tissue abscesses. METHODS: PubMed, Scopus, CINAHL, LILACS, Google Scholar, the Cochrane Database of Systematic Reviews, the Cochrane Central Register of Controlled Trials, and bibliographies of selected articles were assessed for all retrospective, prospective observational, and randomized controlled trials comparing treatment failures between LDT and CID among patients with skin and soft tissue abscesses. Data were dual extracted into a predefined worksheet and quality analysis was performed using the Cochrane Risk of Bias tool or the Newcastle-Ottawa scale. Data were summarized and presented as odds ratios (ORs) with 95% confidence intervals (CIs). Subgroup analyses were performed for adult and pediatric patients. RESULTS: A total of 1,374 studies were identified with eight studies (n = 910 patients) selected for inclusion. Overall, CID failed in 69 of 487 patients (14.17%), while LDT failed in 35 of 423 patients (8.27%). There was an OR of 2.02 (95% CI = 1.29 to 3.18) in favor of higher failures in the CID group. This finding remained consistent with only randomized controlled trials (OR = 1.75, 95% CI = 1.07 to 2.86), but no difference was identified in the adult or pediatric subgroups. CONCLUSION: The LDT was associated with reduced treatment failures when compared with CID. Future studies should further assess the impact on pain, cosmetic outcomes, and health care costs.
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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 | 0.014 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.041 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".