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Record W2070212329 · doi:10.1177/0022034511423396

Implant Overdentures and Nutrition

2011· article· en· W2070212329 on OpenAlexafffund
Manal Awad, João Morais, Stephanie D. Wollin, Abdelouahed Khalil, Katherine Gray‐Donald, Jocelyne S. Feine

Bibliographic record

VenueJournal of Dental Research · 2011
Typearticle
Languageen
FieldMedicine
TopicFolate and B Vitamins Research
Canadian institutionsHealth and Social Services Centre University Institute of Geriatrics of SherbrookeMcGill UniversityRoyal Victoria Hospital
FundersCanadian Institutes of Health ResearchMcGill University Health CentreMcGill University
KeywordsDentistryImplantOrthodonticsMedicineSurgery

Abstract

fetched live from OpenAlex

We conducted a randomized clinical trial to determine whether providing simple mandibular implant overdentures (IODs) to elderly individuals would give them a significantly better nutritional profile than those who receive complete dentures (CDs). Two hundred fifty-five edentate patients > 65 yrs were randomly assigned to receive maxillary CDs and mandibular IODs (n = 128) or CDs (n = 127). Six-month and one-year post-treatment outcomes were blood plasma levels of homocysteine (tHcy), vitamin B12, vitamin B6, albumin, serum folate, and C-reactive protein concentrations, as well as dietary intake. The association between treatment and tHcy levels was not statistically significant. A decline of folate from baseline values in both study groups, as well as those of vitamins B6 and B12 and albumin, was observed. Significant between-group differences were detected in food preparation and in the individuals' ability to chew a variety of foods. This study suggests that implant overdentures do not have a more positive effect on the nutritional state of elderly edentate individuals at 6 and 12 mos post-treatment than new complete dentures. However, those wearing IODs are significantly more likely to take in their nutrients through fresh, whole fruits and vegetables.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.509
Threshold uncertainty score0.306

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.144
GPT teacher head0.430
Teacher spread0.286 · 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

Citations43
Published2011
Admission routes2
Has abstractyes

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