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Nutritional and anthropometric analysis of edentulous patients wearing implant overdentures or conventional dentures

2008· article· en· W2109333920 on OpenAlexaff
Katia Müller, José Morais, Jocelyne S. Feine

Bibliographic record

VenueBrazilian Dental Journal · 2008
Typearticle
Languageen
FieldDentistry
TopicDental Implant Techniques and Outcomes
Canadian institutionsRoyal Victoria HospitalRoyal Victoria Regional Health CentreMcGill University
Fundersnot available
KeywordsDenturesDentistryAnthropometryMedicineOrthodontics

Abstract

fetched live from OpenAlex

As masticatory efficiency diminishes drastically in edentulous patients, several researchers have studied over the past two decades how dietary intake varies when different types of oral rehabilitation are provided. Since the use of implants to support prostheses in edentulous mandibles has been shown to significantly improve masticatory performance, the question remains as to whether this improvement will influence the nutritional status. The purose of this study was to evaluate the nutritional status of edentulous patients who randomly received either a mandibular conventional denture (CD) or an implant-supported overdenture (IP) 1 year previously. Weight, height, body composition and handgrip strength measurements were collected for analysis. Blood tests were performed to measure plasma parameters of diet intake. Participants responded to a Food Frequency Questionnaire and a Masticatory Function Questionnaire. Fifty-three people participated (58% men, 42% women; mean age = 53). Body composition indicators as well as plasma parameters were generally within normal range, and no statistically significant difference (p>0.05) was found between the groups. Patients in the CD group had significantly lower ratings for items regarding difficulty in chewing (p<0.05), but no significant difference was found for dietary intake (p>0.05). Although the CD wearers reported having more difficulty in chewing hard foods, both groups appeared to have a similar nutritional status.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.019
GPT teacher head0.301
Teacher spread0.282 · 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.

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

Citations75
Published2008
Admission routes1
Has abstractyes

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