General health status of Dutch elderly receiving implant‐retained overdentures: A 9‐year big data cross‐sectional study
Bibliographic record
Abstract
BACKGROUND: Very little information is available on the general health of elderly who are provided with an implant-retained overdenture (IOD). PURPOSE: The general health status of three groups of elderly (≥75 years) were compared: those with a natural dentition (ND), those treated with an implant-retained overdenture (IOD), and those wearing a conventional denture (CD). MATERIALS AND METHODS: Data on healthcare costs were obtained from records of Dutch health insurers that are collected by Vektis. Data on general health (chronic diseases, medication use, and polypharmacy) were acquired for elderly patients with a ND, an IOD, and a CD in 2009 and 2017. Data on the general health of elderly who received an IOD were also acquired from 2010 through 2016. RESULTS: On average, the general health of elderly who received an IOD was comparable to general health of elderly with a ND and was better than the general health of elderly with a CD (lower prevalence of diabetes, cardiac disease, and hypertension). The general health profile of elderly receiving an IOD was consistent during all years. CONCLUSIONS: The general health of elderly with a ND or IODs is better than those with CDs.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".