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Record W7132885066

Self-reported Oral Health Status and Diabetes Outcomes in a Cohort of Diabetics in Ontario, Canada

2019· dissertation· W7132885066 on OpenAlexaboutno aff
Kamini Kaura Parbhakar

Bibliographic record

VenueTSpace · 2019
Typedissertation
Language
FieldDentistry
TopicOral microbiology and periodontitis research
Canadian institutionsnot available
Fundersnot available
KeywordsOddsCohortLogistic regressionOdds ratioDiabetes mellitusOral healthCohort studyEpidemiology
DOInot available

Abstract

fetched live from OpenAlex

Objectives: To determine the difference in complications among diabetics reporting oral health. Methods: A cohort study was undertaken of diabetics from the CCHS (2003; 2007-8). Self-reported oral health (SROH) was linked to health records for participants aged > 40 years (N=5,183). A series of hazards and logistic regressions were constructed to determine the risk and odds of acute/chronic complications. Participants without complications were censored at death or at March 31/ 2016. Models were adjusted for age, sex, social and behavioural factors. Results: Those reporting “poor-fair” oral health had a 30% hazard of a complication (HR 1.29 95%CI 1.03, 1.61), and a 10% odds of acute (OR 1.10 95%CI 0.81, 1.51) and 34% odds of chronic complications (OR 1.34 95%CI 1.11, 1.61), greater than those reporting “good-excellent” oral health Conclusion: Oral health status is associated with diabetes complication; this study is the first to explore the oral health-diabetes link in Ontario.

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)0.000
metaresearch head score (Gemma)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.097

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
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 head0.021
GPT teacher head0.342
Teacher spread0.321 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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