The Impact of Tooth Retention on Health and Quality of Life in Older Adults
Bibliographic record
Abstract
Abstract America is aging rapidly, and older adults (age ≥65 y) are retaining more of their natural teeth, a trend expected to continue. Although much is known about the impact of complete tooth loss on overall health and well-being, less is known about the effect of partial tooth loss. We conducted a systematic review to advance our understanding of the impact of retaining ≥20 teeth on health and quality of life (QoL) in older adults using two tooth retention concepts – shortened dental arch (SDA) and functional dentition (FD). We searched seven scientific databases from 1981–2019 for publications on tooth retention and outcomes and impact on health and QoL. Ninety-six studies were included in this review. Most were assessed with low risk of bias (n=74) and of good quality (n=73) using the revised Cochrane Risk of Bias tool and Newcastle-Ottawa Scale. Tooth retention was defined as FD in 82 studies, SDA in 10 studies, and four studies used both. Most were cross-sectional and only seven were from the US. We found an increasing trend among published studies in using FD and SDA to describe natural dentition retention (50 articles in 2015-19 vs one in 1995-99). In general, having <20 teeth was associated with increased likelihood for functional dependence, onset of disability, declines in higher-level functioning, and lower QoL. New information is needed to facilitate clinical decision-making, care-giving, and to help health providers better meet the future oral health needs of an aging US population.
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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 | 0.008 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".