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Record W2539364912 · doi:10.1111/cid.12455

Proton Pump Inhibitors and the Risk of Osseointegrated Dental Implant Failure: A Cohort Study

2016· article· en· W2539364912 on OpenAlexafffundvenueabout
Xixi Wu, Khadijeh Al‐Abedalla, Samer Abi‐Nader, Nach G. Daniel, Belinda Nicolau, Faleh Tamimi

Bibliographic record

VenueClinical Implant Dentistry and Related Research · 2016
Typearticle
Languageen
FieldMedicine
TopicOrthopaedic implants and arthroplasty
Canadian institutionsUniversité de MonctonMoncton HospitalMcGill University
FundersCanadian Institutes of Health ResearchChina Scholarship Council
KeywordsOsseointegrationMedicineImplant failureDentistryDental implantImplantRetrospective cohort studyHazard ratioInternal medicineProportional hazards modelConfoundingCohortCohort studySurgeryConfidence interval

Abstract

fetched live from OpenAlex

Proton pump inhibitors (PPIs) have a negative impact on bone accrual. Because osseointegration is influenced by bone metabolism, this study investigates the association between PPIs and the risk of osseointegrated implant failure. This retrospective cohort study included a total of 1,773 osseointegrated dental implants in 799 patients (133 implants in 58 PPIs users and 1,640 in 741 non-users) who were treated at the East Coast Oral Surgery Clinic in Moncton, Canada, from January 2007 to September 2015. Kaplan-Meier estimator was used to describe the hazard function of dental implant failure by PPIs usage. Multilevel mixed effects parametric survival analyses were used to test the association between PPIs exposure and risk of implant failure adjusting for potential confounders. The failure rates were 6.8% for people using PPIs compared to 3.2% for non-users. Subjects using PPIs had a higher risk of dental implant failure (HR = 2.73; 95% CI = 1.10-6.78) compared to those who did not use the drugs. The findings suggest that treatment with PPIs may be associated with an increased risk of osseointegrated dental implant failure.

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)0.007
metaresearch head score (Gemma)0.002
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 score0.092
Threshold uncertainty score0.914

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.041
GPT teacher head0.394
Teacher spread0.353 · 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

Citations59
Published2016
Admission routes4
Has abstractyes

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