MétaCan
Menu
Back to cohort
Record W2331770093 · doi:10.1177/2380084416630508

Efficacy of Mandibular 2-implant Overdenture

2016· article· en· W2331770093 on OpenAlexafffund
Naoki Kodama, Balendra Pratap Singh, Daiane Cerutti‐Kopplin, Jocelyne S. Feine, Elham Emami

Bibliographic record

VenueJDR Clinical & Translational Research · 2016
Typearticle
Languageen
FieldDentistry
TopicDental Implant Techniques and Outcomes
Canadian institutionsUniversité de MontréalMcGill University
FundersFonds de Recherche du Québec - SantéCanadian Institutes of Health Research
KeywordsMedicineRandomized controlled trialDenturesMeta-analysisConfidence intervalDentistryImplantMEDLINEPatient satisfactionCochrane LibraryOral healthInternal medicineSurgery

Abstract

fetched live from OpenAlex

The extent of heterogeneity regarding the efficacy of the mandibular 2-implant overdenture is still in question. The aim of this meta-analysis is to provide an update on the existing evidence from randomized controlled trials assessing the efficacy of the mandibular 2-implant overdenture in regard to patient-based outcomes. Electronic searches were carried out to September 2015 through MEDLINE, EMBASE, the Cochrane Central Register of Controlled Trials, and the Cochrane Systematic Reviews. Only randomized controlled trials that compared conventional dentures with mandibular 2-implant overdentures were included. Patient-based outcomes were assessed, including patient satisfaction and oral health– and general health–related quality of life. Random effects models were used to pool the effect sizes of all included studies. Further stratified analyses and heterogeneity analyses were tested, as was publication bias. In addition to the 7 randomized controlled trials that were included in the previous meta-analysis, 4 new trials were identified and included in this update. A random effects model showed that, when compared with conventional dentures, mandibular 2-implant overdentures significantly improved patient satisfaction (pooled effect size = 0.87, z = 5.31, 95% confidence interval: 0.55 to 1.19, P < 0.0001, χ 2 = 41.82, df = 8, P < 0.0001, I 2 = 81%) and oral health–related quality of life (pooled effect size = −0.66, z = 2.72, 95% confidence interval: –1.13 to −0.18, P = 0.007, χ 2 = 21.26, df = 4, P = 0.0003, I 2 = 81%). The differences in participant recruitment and their pretreatment condition were important sources of heterogeneity among the studies. Only 1 study investigated the impact of mandibular implant overdentures on perceived general health, and it revealed no between-treatment differences. The 2-implant mandibular overdenture improves patient satisfaction and quality of life for the general edentate population. Health status, poor oral condition, and patient characteristics may effect patient-based outcomes and should be considered by clinicians in treatment planning. Knowledge Transfer Statement: This meta-analysis shows that mandibular 2-implant overdentures are significantly more satisfactory to the general edentate populations than new conventional dentures. The results also show that mandibular 2-implant overdentures provide significantly better oral health–related quality of life than do new conventional dentures. These results should be shared with edentate patients in planning their 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)0.018
metaresearch head score (Gemma)0.026
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.018
Threshold uncertainty score0.095

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.026
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0110.031
Bibliometrics0.0030.002
Science and technology studies0.0000.001
Scholarly communication0.0030.001
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.234
GPT teacher head0.534
Teacher spread0.300 · 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

Citations14
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueJDR Clinical & Translational ResearchSame topicDental Implant Techniques and OutcomesFrench-language works237,207