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Record W4205256300 · doi:10.1111/cid.13063

Peripheral giant cell granuloma associated with dental implants: Case‐series

2022· article· en· W4205256300 on OpenAlexvenueno aff
Samar Abofoul, Ayelet Zlotogorski‐Hurvitz, Osnat Grinstein‐Koren, Amir Shuster, Marilena Vered, Jeremy Edel, Ilana Kaplan

Bibliographic record

VenueClinical Implant Dentistry and Related Research · 2022
Typearticle
Languageen
FieldDentistry
TopicOral and Maxillofacial Pathology
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineImplantGiant cellDentistryLesionDental implantSurgeryPathology

Abstract

fetched live from OpenAlex

PURPOSE: The objectives were to characterize clinico-pathologically a large series of peri-implant peripheral giant cell granuloma (PGCG), and investigate the role of foreign material as a possible etiological factor. MATERIAL AND METHODS: The study was retrospective, conducted on peri-implant specimens submitted for histology between 2005 and 2021. RESULTS: Three hundred and thirty-five peri-implant biopsies were retrieved, of which 52 (15.5%) were PGCG. The study population included 28 females and 24 males, age 35-92 years, mean 61. 51.2% reported bone involvement. The lesion involved the margins of the specimen in 65.3%, recurrence was reported in 46.1%. In 58.8% the implant was removed at the same time the specimen was submitted for histopathological analysis. Small foci of black granular foreign material were observed in 53.8% of cases of which 67.8% were birefringent under polarized light. The foreign material granules were not ingested inside multinucleated giant cells, but were scattered in the stromal compartment. CONCLUSIONS: Peri-implant PGCG is locally aggressive, with frequent bone involvement and high recurrence rate, resulting in implant loss in the majority of cases. The high recurrence rate may be related to conservative or inadequate surgery. Foreign material although common does not seem to have a role in its development.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.003
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 head0.077
GPT teacher head0.396
Teacher spread0.319 · 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.

Study designCase report
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

Citations5
Published2022
Admission routes1
Has abstractyes

Explore more

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