Dental Implant Status of Patients Receiving Long‐Term Nursing Care in <scp>J</scp>apan
Bibliographic record
Abstract
BACKGROUND: The increase in implant patients is expected to give rise to a new problem: the changing general health status of those who have had implants placed. PURPOSE: The aim of this present study was to find out the needs of and proper measures for elderly implant patients in long-term care facilities. MATERIALS AND METHODS: A questionnaire was sent by mail to 1,591 long-term care health facilities, daycare services for people with dementia, and private nursing homes for the elderly in the Osaka area, which is in the middle area of Japan, in order to extract patients with cerebrovascular disease or dementia who were possibly at risk of inadequate oral self-care, as well as patients with implants. RESULTS: Approximately half of all facilities responded that they cannot recognize implants, and many facilities did not know anything about oral care for implant patients. Residents with implants were reported at 19% of all facilities. Also, the facilities pointed out problems with implants relating to the difference in oral care between implants and natural teeth. CONCLUSIONS: There are people with implants in some 20% of caregiving facilities, and there is a low level of understanding regarding implants and their care among nurses and care providers who are providing daily oral care.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".