Impact of WHO's Surgical Safety Checklist-Based Program on Cleft-lip and Palate Repair Outcomes in LMICs—The CLEAN CLEFT Program
Bibliographic record
Abstract
Background “Clean Cleft” (CC) is an adaptation of the Lifebox Clean Cut program, designed to reduce surgical site infections (SSIs) in cleft lip and palate repairs. It focuses on 6 key processes: hand and site decontamination, surgical linen integrity, instrument sterility, timely antibiotic use, gauze counting, and WHO Surgical Safety Checklist compliance. The study explores CC's effectiveness in reducing infections, other complications, and enhancing early recovery. Methods CC was piloted in 2 Ethiopian hospitals and 1 in Côte d'Ivoire, the primary public cleft care centers in each country. Baseline data were collected through direct observation in the operating room, with patients monitored postoperatively for infections and complications through daily ward visits and follow-up calls or clinic visits at 30 days. Post-intervention data were collected for 5 months. Data was captured in DHIS2 software and analyzed using SPSS version 26. Results The program enrolled 275 patients, with 156 during baseline and 119 post-implementation. Complications significantly dropped from 21.7% to 8.7% ( P = .008), a 60% decrease. SSI rates fell from 18.1% to 8.0% ( P = .03), while palatal fistulas decreased from 13.0% to 6.1% ( P = .1) and wound dehiscence from 18.0% to 8.0% ( P = .03). Adherence to perioperative standards improved, except for hand and skin preparation while pain management remained effective throughout the program. Conclusion CC improved perioperative practices, significantly reducing infections, palatal fistulas, and wound dehiscence, supporting the broader program expansion to any subspecialty.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".