Systematic review of longitudinal studies on the association between cluster of health-related behaviors and tooth loss among adults
Bibliographic record
Abstract
To systematically review longitudinal studies on the association between cluster of/multiple health-related behaviors and tooth loss among adults. Inclusion criteria were prospective and retrospective longitudinal studies; adults; multiple or cluster of behaviors; tooth loss, one or more tooth lost and complete tooth loss. Exclusion criteria were intervention studies; cross-sectional studies; case-control studies; children under 18 years-old; single behavior. Two reviewers searched three databases up to April 2023. Open Grey and Google Scholar were searched for grey literature. Twelve longitudinal studies were included in this review. Nine studies had good quality, two had poor quality, and one had fair quality according to New-Castle-Ottawa Scale. According to ROBINS-E tool, nine studies were judged as moderate risk of bias while two studies were at low risk of bias and one study had serious risk of bias. One study assessed cluster of behavior, while others examined a number of separate health-related behaviors in relation to tooth loss. Meta-analysis was not feasible because of the high heterogeneity in exposure, measure of outcomes, covariates, sample size, and follow-up time. The research found an association between tooth loss and oral hygiene practices (two studies), dental attendance (four studies), smoking (six studies), and alcohol consumption (three studies). This review provides evidence of a longitudinal association between cluster of/multiple health related-behaviors and tooth loss.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.081 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.008 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".