Patient Safety and Quality Improvement Initiatives in Cleft Lip and Palate Surgery: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Cleft lip and/or palate repair techniques require continued reevaluation of best practice through high-quality evidence. The objective of this systematic review was to highlight the existing evidence for patient safety and quality improvement (QI) initiatives in cleft lip and palate surgery. METHODS: A systematic review of published literature evaluating patient safety and QI in patients with cleft lip and/or palate was conducted from database inception to June 9, 2022, using Preferred Reporting Items for Systematic Reviews guidelines. Quality appraisal of included studies was conducted using Methodological Index for Non-Randomized Studies, Cochrane, or a Measurement Tool to Assess Systematic Reviews (AMSTAR) 2 instruments, according to study type. RESULTS: Sixty-one studies met inclusion criteria, with most published between 2010 and 2020 (63.9%). Randomized controlled trials represented the most common study design (37.7%). Half of all included studies were related to the topic of pain and analgesia, with many supporting the use of infraorbital nerve block using 0.25% bupivacaine. The second most common intervention examined was use of perioperative antibiotics in reducing fistula and infection (11.5%). Other studies examined optimal age and closure material for cleft lip repair, early recovery after surgery protocols, interventions to reduce blood loss, and safety of outpatient surgery. CONCLUSIONS: Patient safety and QI studies in cleft surgery were of moderate quality overall and covered a wide range of interventions. To further enhance PS in cleft repair, more high-quality research in the areas of perioperative pharmaceutical usage, appropriate wound closure materials, and optimal surgical timing are needed.
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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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".