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Record W4391646888 · doi:10.3390/dj12020036

Appointments Needed for Complete Denture for Frail Older Adults Residing in Long-Term Care Facilities: A Cross-Sectional Study

2024· article· en· W4391646888 on OpenAlexaff
Sahr H. Altuwaijri, Tharee Champirat, Chris Wyatt

Bibliographic record

VenueDentistry Journal · 2024
Typearticle
Languageen
FieldDentistry
TopicDental Health and Care Utilization
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsDenturesMedicineCross-sectional studyDentistryLong-term careDental careDental prosthesisImplantNursing

Abstract

fetched live from OpenAlex

Frail older adults who reside in long-term care (LTC) facilities face multiple barriers in receiving dental care. In edentulous LTC patients, the fabrication of complete dentures (CDs) can present challenges, leading to an increase in procedural or post-insertion appointments. The aim of this cross-sectional study was to document the number of fabrication and post-insertion follow-up appointments for CDs in frail older adults residing in LTC facilities. Data were collected from electronic patient records (AxiUm) and the Index of Clinical Oral Disorder in Elders (CODE) software utilized by the University of British Columbia Geriatric Dentistry Program from 2002 to 2018. A total of 362 CDs were fabricated between 2002 and 2018 in 272 patients. The mean number of visits required was 4.13 and 4.32, with standard deviations (Std) of 1.45 and 1.25 needed to fabricate maxillary CDs and mandibular CDs, respectively. The mean number of follow-up visits was 1.04 for maxillary dentures and 1.09 for mandibular dentures, with an Std of 1.25 for both, similar to the results obtained for adult patients in community dental clinics. Several factors were found to be associated with an increased number of CD fabrication and follow-up visits. Pre-operative assessment of the patient's cognitive/physical status and intra-oral condition may indicate the estimated time needed to fabricate CDs.

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 categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.037
GPT teacher head0.373
Teacher spread0.336 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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