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Record W4311972898 · doi:10.1111/cid.13171

Palatal soft tissue thickness around dental implants and natural teeth in health and disease: A cross sectional study

2022· article· en· W4311972898 on OpenAlexvenueno aff
Hiba Abu Hussien, Eli E. Machtei, Alaa Khutaba, Eran Gabay, Hadar Zigdon‐Giladi

Bibliographic record

VenueClinical Implant Dentistry and Related Research · 2022
Typearticle
Languageen
FieldDentistry
TopicDental Implant Techniques and Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineDentistryGingivitisSoft tissueImplantPeri-implantitisMucositisMaxillaPeriodontal probeBleeding on probingPeriodontitisOrthodonticsSurgery

Abstract

fetched live from OpenAlex

BACKGROUND: Previous studies focused on the influence of buccal mucosa thickness on peri-implant bone loss and inflammation, with inconclusive results. We observed substantially thicker palatal mucosal tissues at peri-implantitis sites. Therefore, we hypothesize that thick palatal peri-implant mucosa may be associated with deeper pockets and disease severity. PURPOSE: To compare the thickness of the palatal tissue between natural teeth and implants in periodontal health and disease. METHODS: Adult, non-smoker, healthy patients who visited our department for periodontal examination or treatment with restored implants in the posterior maxilla were recruited. Probing depth (PD), plaque index (PI), gingival index (GI) and radiographic measurements were recorded around implant and the contralateral tooth. Palatal tissue thickness was measured using a 30G needle that was inserted perpendicular into the mucosa at the bottom of the periodontal/peri-implant pocket and 3 mm coronally. Differences in the palatal tissue thickness between teeth and implants (in the same patient) was performed using t-test; as well as between peri-implantitis and non-peri-implantitis sites (among patients). RESULTS: Sixty patients were included. Thirty-four implants were diagnosed with peri-implantitis and 26 healthy/mucositis implants with corresponding 24 healthy/gingivitis teeth and 36 teeth with attachment loss. Mean PD was higher around implants (4.47 ± 1.57 mm) than teeth (3.61 ± 1.23 mm, p = 0.001). The thickness of implants' palatal mucosa was higher than in teeth, at the base of the pocket and 3 mm coronally (4.58 ± 1.38 mm vs. 3.01 ± 1.11, p = 0.000; 3.58 ± 2.15 vs. 1.89 ± 1.11, p = 0.000, respectively). Mean palatal tissue thickness was 4.32 ± 2.35 mm for the peri-implantitis group while only 2.61 ± 1.39 in healthy implants, 3 mm coronal to the base of the pocket (p = 0.001). Palatal thickness at peri-implantitis sites was higher (4.32 ± 2.35) compared to periodontitis sites (2.23 ± 0.93), p = 0.000. Implant sites with palatal mucosa >4 mm (n = 32) had deeper mean pockets (5.58 ± 1.98) compared with thinner (≤4 mm) sites (n = 28) (4.48 ± 1.18, p = 0.018). CONCLUSION: Thicker palatal tissue around implants is associated with deeper palatal pockets. Thick palatal tissue was found around implants diagnosed with peri-implantitis.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.102
GPT teacher head0.499
Teacher spread0.397 · 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 source (direct Gemma or distilled Codex), 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

Citations9
Published2022
Admission routes1
Has abstractyes

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