Tooth Loss and Nutritional Status in Older Adults: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Background/Objective: Older adults are at risk for tooth loss and compromised nutritional status. Our objective was to conduct a systematic review and meta-analysis to answer the following question: Among adults aged ≥60 y living in developed countries, what are the associations between tooth loss and nutritional status as assessed by a validated nutrition screening or assessment tool? Methods: PRISMA guidelines were followed. PubMed, Scopus, CINAHL, Web of Science, and MEDLINE were searched for studies published in English between 2009 and 2019 that met inclusion criteria. Data extracted included study and participant characteristics, dentition, and nutritional status. Risk of bias was assessed with a modified Newcastle-Ottawa Scale. Random effects meta-analysis was used. Results: Of the 588 unduplicated articles identified, 78 were reviewed in full text, and 7 met inclusion criteria. Six studies were combined for a meta-analysis, which revealed that individuals who were completely edentulous or who lacked functional dentition had a 21% increased likelihood of being at risk of malnutrition or being malnourished, as compared with those who were dentulous or had functionally adequate dentition (risk ratio, 1.21; 95% CI, 1.11 to 1.32; I 2 = 70%). Whether the article statistically adjusted for medical history explained most of the heterogeneity in the pooled effect. Conclusions and Implications: Findings suggest that older adults with tooth loss are at greater risk of malnutrition than those with functionally adequate dentition. Use of validated tools to assess risk of malnutrition in older adults with tooth loss is important to promote early intervention and referral to optimize nutrition and oral health status. Findings were limited by heterogeneity, risk of bias, and overall quality of the studies reviewed. Cohort studies that adjust for known confounders and use consistent approaches to assess tooth loss and nutritional status are needed. Knowledge Transfer Statement: The results of this study suggest that older adults with tooth loss are at greater risk of malnutrition than those with functionally adequate dentition. Screening of this population for malnutrition by health care professionals, including dentists and dietitians, may result in corresponding referrals to optimize nutrition and oral health status. Further research is needed with consistent approaches to assess tooth loss and nutritional status.
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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 | 0.015 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.035 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".