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Record W2077087576 · doi:10.1111/clr.12205

The effect of mandibular 2‐implant overdentures on oral health–related quality of life: an international multicentre study

2013· article· en· W2077087576 on OpenAlexaff
Manal Awad, Faahim Rashid, Jocelyne S. Feine

Bibliographic record

VenueClinical Oral Implants Research · 2013
Typearticle
Languageen
FieldDentistry
TopicDental Implant Techniques and Outcomes
Canadian institutionsMcGill University
Fundersnot available
KeywordsDenturesMedicineDentistryImplantQuality of life (healthcare)Oral healthOrthodonticsNursingSurgery

Abstract

fetched live from OpenAlex

OBJECTIVES: To determine the difference in oral health-related quality of life (OHRQoL) in patients who received mandibular 2-implant overdentures and conventional dentures in a pragmatic international study. MATERIALS AND METHODS: In this prospective study, data were gathered from 203 edentulous patients (mean age, 68.8; SD: 10.4 years) at eight centres in North America, South America and Europe. The patients were provided with new mandibular conventional dentures or implant overdentures supported by 2 implants and ball attachments and opposed by conventional dentures. At baseline and at 6 months post-treatment, patients rated their oral health-related quality of life using the OHIP-20. RESULTS: A significantly higher proportion of the participants in the implant group in North America reported improvement in both the psychological and the handicap domains, compared to those who received conventional dentures (93% vs. 52%; P < 0.05). In South America, 100% of participants who received implant overdentures reported improvement in physical pain, compared to 66% in the conventional group (P < 0.05). Differences in mean change scores among those who expressed improvement were not significantly different between sites or treatments. CONCLUSION: Mandibular 2-implant overdentures are more likely than conventional dentures to improve OHRQL for edentulous patients. Cultural differences were also observed in the impact of implant overdentures on the different domains of the OHIP-20.

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)0.010
metaresearch head score (Gemma)0.003
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 score0.054
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.290
GPT teacher head0.590
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 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

Citations94
Published2013
Admission routes1
Has abstractyes

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