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Record W4400912761 · doi:10.12968/denu.2024.51.7.476

An apically positioned flap and free gingival graft around an implant

2024· article· en· W4400912761 on OpenAlexaff
Matthew K Morris, Debora Matthews, Richard Bengt Price

Bibliographic record

VenueDental Update · 2024
Typearticle
Languageen
FieldMedicine
TopicPeriodontal Regeneration and Treatments
Canadian institutionsDalhousie University
Fundersnot available
KeywordsDentistryImplantMedicineGingival and periodontal pocketOrthodonticsSurgeryPeriodontal disease

Abstract

fetched live from OpenAlex

Following the ‘2017 World Workshop on the Classification of Periodontal and Peri-Implant Diseases and Conditions,’ hard and soft tissue deficiencies have been debated in the literature with keratinized mucosa at the forefront. In this case report, the present author investigated whether an apically positioned flap and free gingival graft to increase keratinized mucosa and vestibular depth improved oral hygiene and patient comfort around an implant-retained upper complete denture. Using an apically positioned flap in combination with free gingival graft to augment the soft tissues around implants with hard and soft tissue deficiencies, increased both the keratinized mucosa and vestibular depth to correct the soft tissue deficiencies. This case report highlights the importance keratinized mucosa and vestibular depth has at the patient and site level in improving oral hygiene and patient comfort. This case report also supports the growing evidence that the ideal periodontal phenotype around implants is to have keratinized mucosa ≥2 mm and vestibular depth ≥4 mm. CPD/Clinical Relevance: This case report supports growing evidence that the ideal periodontal phenotype around implants is to have keratinized mucosa ≥2 mm and vestibular depth ≥4 mm.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.659
Threshold uncertainty score0.363

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.010
GPT teacher head0.294
Teacher spread0.283 · 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 designBench or experimental
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
Published2024
Admission routes1
Has abstractyes

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