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Record W2579898359 · doi:10.3390/dj5010008

Oral Health, Nutritional Choices, and Dental Fear and Anxiety

2017· review· en· W2579898359 on OpenAlexaff
Jennifer R. Beaudette, Péter Fritz, Philip Sullivan, Wendy E. Ward

Bibliographic record

VenueDentistry Journal · 2017
Typereview
Languageen
FieldDentistry
TopicDental Anxiety and Anesthesia Techniques
Canadian institutionsBrock University
Fundersnot available
KeywordsAnxietyOral healthPsychologyMedicineDentistryPsychiatry

Abstract

fetched live from OpenAlex

Oral health is an integral part of overall health. Poor oral health can lead to an increased risk of chronic diseases including diabetes mellitus, cardiovascular disease, and some types of cancer. The etiology of these diseases could be linked to the individual's inability to eat a healthy diet when their dentition is compromised. While periodontal or implant surgery may be necessary to reconstruct tissue around natural teeth or replace missing teeth, respectively, some individuals avoid such interventions because of their associated fear and anxiety. Thus, while the relationship between poor oral health, compromised nutritional choices and fear and anxiety regarding periodontal procedures is not entirely new, this review provides an up-to-date summary of literature addressing aspects of this complex relationship. This review also identifies potential strategies for clinicians to help their patients overcome their fear and anxiety associated with dental treatment, and allow them to seek the care they need.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.097
GPT teacher head0.400
Teacher spread0.304 · 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 designNot applicable
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

Citations46
Published2017
Admission routes1
Has abstractyes

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