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Record W4403513671 · doi:10.4103/jfmpc.jfmpc_328_24

Oral health inequalities among geriatric population: A systematic review

2024· review· en· W4403513671 on OpenAlexaboutno aff
Aseema Samal, Ipseeta Menon, Kunal Kumar Jha, Gunjan Kumar, Arpita Singh

Bibliographic record

VenueJournal of Family Medicine and Primary Care · 2024
Typereview
Languageen
FieldDentistry
TopicDental Health and Care Utilization
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePopulationGerontologyOral healthProtocol (science)Health careMEDLINEInequalityQuality of life (healthcare)Family medicineScale (ratio)Environmental healthAlternative medicineNursingPathology

Abstract

fetched live from OpenAlex

As per the World Health Organization, governments should aim to accomplish two significant global milestones by 2030: reducing health disparities and granting universal accessibility to healthcare. The aim of this article was to systematically review the inequalities and understand the multifactorial causation of oral health inequalities among the older adults. Methods: Preferred Reporting Items for Systematic Reviews and Meta Analyses (PRISMA) standards were used to carry out the review and is documented in PROSPERO CRD42026695761. Two authors did the search and screening in accordance with the protocol. Electronic databases such as PubMed, Google Scholar, and EBSCOhost articles of the last 10 years were searched for research presenting data on oral health status and oral health related quality of life in the elderly population. Quality assessment was performed using the Newcastle Ottawa Scale (NOS) for retrospective and prospective research. Results: Only 24 studies fulfilled the eligibility criteria and were incorporated into the qualitative synthesis. Multiple aspects of oral health and the related variables influencing disparities in oral health in the elderly population living in institutions have a positive link. Conclusion: The findings support the notion that this demographic consists of weak, dependent individuals who have poor oral health. The vulnerable elderly institutionalized population was recognized and validated, thus helps in providing measures that will eventually focus the risk factors to improve their OHRQoL.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.145
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0010.001
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 head0.113
GPT teacher head0.428
Teacher spread0.315 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations6
Published2024
Admission routes1
Has abstractyes

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