MétaCan
Menu
Back to cohort
Record W3118524654 · doi:10.6026/97320630016974

Concerns on oral health care services for adults with cognitive and intellectual disabilities

2020· article· en· W3118524654 on OpenAlexaboutno aff
Meignana Arumugham Indiran

Bibliographic record

VenueBioinformation · 2020
Typearticle
Languageen
FieldDentistry
TopicDental Health and Care Utilization
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)MedicineIntellectual disabilityProtocol (science)Health careOral healthAgency (philosophy)Scale (ratio)Family medicineDental careMEDLINECognitionNursingMedical educationAlternative medicinePsychiatry

Abstract

fetched live from OpenAlex

It is of interest to document data on oral health care services for adults with cognitive and intellectual disabilities. Hence, a study protocol was registered at the International Prospective Register of Systematic Reviews (PROSPERO) with registration number: CRD42020150759. We used PubMed, Science Direct, LILACS and SCIELO to collect data from known literature using keywords containing MESH (Medical Subject Headings) terms. The risk of bias rating for the collected data was calculated using the Newcastle-Ottawa assessment Scale. The AHRQ (Agency for Healthcare, Research and Quality) was used for classifying the level of evidence in the collected data. Analysis of available data shows that there is a lack of dentists with adequate skills to treat people with disabilities resulting in high cost for dental treatment. Thus, we conclude that inconvenient location of dental clinic, lack of dentists willing to treat people with disabilities and attitude of dental staff towards people with learning disabilities were considered as barriers and challenges faced for dental health service utilization in this context.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.020
metaresearch head score (Gemma)0.113
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.020
Threshold uncertainty score0.104

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.113
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0040.006
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0080.000

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 head0.030
GPT teacher head0.316
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 source (direct Gemma or distilled Codex), 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

Citations6
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueBioinformationSame topicDental Health and Care UtilizationFrench-language works237,207