MétaCan
Menu
Back to cohort
Record W3003764366 · doi:10.1155/2020/6141493

Anti-Vascular Endothelial Growth Factors as a Potential Risk for Implant Failure: A Clinical Report

2020· article· en· W3003764366 on OpenAlexafffund
Elham Emami, Pierre de Grandmont, Mélanie Menassa, Nicholas Audy, Robert Durand

Bibliographic record

VenueCase Reports in Medicine · 2020
Typearticle
Languageen
FieldMedicine
TopicRetinal Diseases and Treatments
Canadian institutionsCegep de Saint JeromeUniversité de MontréalMcGill University
FundersCanadian Institutes of Health ResearchFonds de Recherche du Québec - SantéInternational Team for ImplantologyDENTSPLY ImplantsUniversité de MontréalMcGill University
KeywordsMedicineImplantOsseointegrationImplant failureDentistrySurgery

Abstract

fetched live from OpenAlex

Knowledge of the risk factors for implant osseointegration is essential for clinical decision-making and optimizing treatment success. This clinical report presents a rare case of implant failure in a patient who received intravitreal injections of a vascular endothelial growth factor (VEGF) inhibitor for the treatment of age-related macular degeneration. Following CARE guidelines, the report presents a case rehabilitated with a mandibular 2-implant overdenture using the immediate-loading protocol and standard procedures. The implants failed within six weeks of immediate loading although primary stability (≥50 Ncm) was achieved during surgery and clinical follow-ups did not show any deviance from standard implant care or patient-related complications. Further investigation suggested that the intake of a VEGF inhibitor may be the cause of failure. This clinical report highlights the importance of systemic risk factors in implant success and their consideration during planning for implant-assisted treatment.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0020.002
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0040.002
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.033
GPT teacher head0.353
Teacher spread0.320 · 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 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

Citations2
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueCase Reports in MedicineSame topicRetinal Diseases and TreatmentsFrench-language works237,207