Perioperative care in orthognathic surgery - A systematic review and meta-analysis for enhanced recovery after surgery
Bibliographic record
Abstract
The aim of this study was to determine whether implementing ERAS (Enhanced Recovery After Surgery) elements/protocols improves outcomes in orthognathic surgery (OGS) compared to conventional care. To achieve this, ERAS-specific perioperative elements were identified and literature on ERAS for OGS was systematically reviewed. Using PRISMA methodology and GRADE approach, 44 studies with 49 perioperative care elements (13 pre-, 15 intra-, 21 postoperative) were analyzed. While 39 studies focused on single elements, only five presented multimodal protocols, with three related to ERAS. Preoperative elements included antimicrobial and steroid prophylaxis and prevention of postoperative nausea and vomiting. Intraoperative aspects, especially anesthesiological, showed high evidence. Outcome parameters were heterogeneous: complications and postoperative pain were well-investigated with high evidence, while length of stay (LOS) and patient satisfaction received low to medium evidence. ICU LOS, healthcare costs, and readmission rates were underreported. The meta-analysis revealed significant results for pain reduction and trends towards fewer complications and shorter LOS in the ERAS group. Overall, ERAS protocols are not established in OMFS, particularly OGS. Further research is needed in pre- and postoperative care and standardized multimodal analgesia. The next step should be developing a comprehensive OGS protocol through a consensus conference and implementing it in clinical practice.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.020 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".