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Record W4411429603 · doi:10.1177/10556656251346720

Does Tooth Removal at the Time of Secondary Alveolar Bone Grafting Influence the Outcome for Cleft Patients?

2025· article· en· W4411429603 on OpenAlexaff
Robert M Conville, Nor Nadia Zakaria, Martin Woods, Golfam Khoshkhounejad

Bibliographic record

VenueThe Cleft Palate-Craniofacial Journal · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCleft Lip and Palate Research
Canadian institutionsSt. Thomas Hospital
Fundersnot available
KeywordsMedicineRetrospective cohort studyDentistrySurgery

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.189
Threshold uncertainty score0.933

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.277
Teacher spread0.268 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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