Does Tooth Removal at the Time of Secondary Alveolar Bone Grafting Influence the Outcome for Cleft Patients?
Bibliographic record
Abstract
Objective To investigate whether tooth removal at the time of secondary alveolar bone grafting (SABG) has any association with the surgical outcome and to assess the overall radiographic outcomes of SABG. Design Single-center retrospective cohort study. Setting Tertiary UK cleft service. Methods Any patient diagnosed with a cleft lip and/or palate aged under 16 years who had a SABG over a 3-year period (January 2021-June 2023) were included in the study. Patients with a craniofacial syndrome or those who had a repeat SABG were excluded. Two trained independent assessors were calibrated to use the Kindelan scoring index. Intra and inter-rater reliability was assessed using kappa statistics at the dichotomous level for the calibrated assessors (success vs failure). Chi-square tests were used to assess any associations ( P < .05). Results Ninety patients and 105 SABG (mean age 10.12 years SD 1.45) were included in the study. The overall success rate of the SABGs included was 85.7%. There were no significant differences in outcomes between those who had tooth extraction at the time of bone grafting ( P > .05). There were also no statistically significant association between cleft type and cleft side and graft success. Conclusions Tooth extraction during the time of SABG surgery did not significantly influence its success. The results demonstrated that the SABG success rate was in line with the UK national outcome data.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".