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 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 | 0.031 | 0.122 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".