Predictors of tooth loss during long‐term periodontal maintenance: a systematic review of observational studies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: To systematically assess the factors influencing tooth loss during long-term periodontal maintenance (PM). METHODS: CENTRAL, MEDLINE and EMBASE were searched up to and including September 2009. Studies limited to patients with periodontitis who underwent periodontal therapy and followed a maintenance care programme for the at least 5 years were eligible for inclusion in this review. Studies were considered for inclusion if they reported data on tooth loss during PM. RESULTS: The search strategy identified 527 potentially eligible articles, of which 13 retrospective case series were included in this review. The risk of bias assessment evaluated by the Newcastle-Ottawa scale showed that eight studies were considered of medium methodological quality and five of low methodological quality. Of 41,404 teeth present after active periodontal treatment, 3919 were lost during PM. The percentages of tooth loss due to periodontal reasons and of patients who did not experience tooth loss varied from 1.5% to 9.8% and 36.0% to 88.5%. Studies' individual outcomes showed that different patient-related factors (i.e. age and smoking) and tooth-related factors (tooth type and location, and the initial tooth prognosis) were associated with tooth loss during PM. CONCLUSIONS: The considerable heterogeneity found among studies did not allow definitive conclusions. Age, smoking and initial tooth prognosis were found to be associated with tooth loss during PM. Overall, patients must be instructed to follow periodic PM and quit smoking (smokers). Prospective cohort studies are required to confirm the possible predictors of tooth loss due to periodontal reasons. The allocation of patients into subgroups according to the type of periodontitis and smoking frequency will allow more accurate evaluations.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.015 | 0.004 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it