Tooth loss from the perspective of studies employing a life course approach: a systematic review
Bibliographic record
Abstract
The life course approach scrutinizes factors that shape the development of diseases over time. Tooth loss, which is influenced by social, behavioral and biological factors, can occur at various stages of life and tends to become more prevalent in later years. This systematic review examined the influence of socioeconomic, psychosocial, biological and behavioral adversities in life on the likelihood of tooth loss. Searches were conducted in the Embase, PubMed, Web of Science, Ovid, PsycINFO, Scopus and LILACS databases. Reference management was performed using EndNote online. The risk of bias was appraised using the Newcastle-Ottawa Scale (NOS). The electronic searches yielded 1366 records, 17 of which (13 cohort and four cross-sectional studies) met the inclusion criteria. According to the NOS, all studies had a low risk of bias. Two studies found a link between a lower education and higher incidence of tooth loss and socioeconomic status exerted a significant influence in 47% of the studies. Disadvantaged socioeconomic trajectories and health-related factors, such as smoking, general health perception and oral health behaviors, increased the likelihood of tooth loss. Factors such as dental visits, a history of toothache and exposure to fluoridated water influenced the likelihood of tooth loss. Individuals who experienced adversities in socioeconomic, behavioral and biological aspects throughout their life course were more prone to tooth loss.
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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 | 0.012 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.005 |
| Bibliometrics | 0.018 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".