Clinical comparison of different flap advancement techniques to periosteal releasing incision in guided bone regeneration: A randomized controlled trial
Bibliographic record
Abstract
OBJECTIVES: To compare Double Flap Incision (DF), Coronally Advanced Lingual Flap (CALF), and Modified Periosteal Releasing Incision (MPRI) to Periosteal Releasing Incision (PRI) in flap advancement, postoperative complications in augmentation using titanium mesh. MATERIAL AND METHODS: Forty patients with partially edentulous posterior mandibles were randomly assigned to the four groups. We evaluated: (a) Flap advancement in mm (Primary outcome). (b) Pain using the Numerical Rating scale (NRS). (c) Swelling using the Visual Analogue Scale (VAS). (d) Exposure in mm and exposure percentage at 1 week to 6 months. RESULTS: The CALF showed the highest mean flap advancement of 19.9 (±5.0) mm while the PRI showed the lowest; 10.2 (±1.7) mm. The difference between groups was statistically significant (P value <.0001). MPRI showed the highest pain score of 5.3 (±1.3) while the DF showed the lowest; 2.39 (±1.7). Swelling did not show a significant difference between groups. MPRI showed the highest exposure mean; 18.6 mm (±26.3) while CALF showed the lowest; 2.5 mm (±4.0). PRI showed the highest exposure percentage; 7.4% (±9.3) while CALF showed the lowest; 0.4% (±0.7). The difference between groups was insignificant. CONCLUSIONS: CALF reported highest advancement, least complications while PRI reported the highest complications.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".