Dental conditions in inpatients with schizophrenia: A large-scale multi-site survey
Bibliographic record
Abstract
BACKGROUND: Clinical relevance of dental caries is often underestimated in patients with schizophrenia. The objective of this study was to examine dental caries and to identify clinical and demographic variables associated with poor dental condition in patients with schizophrenia. METHODS: Inpatients with schizophrenia received a visual oral examination of their dental caries, using the decayed-missing-filled teeth (DMFT) index. This study was conducted in multiple sites in Japan, between October and December, 2010. A univariate general linear model was used to examine the effects of the following variables on the DMFT score: age, sex, smoking status, daily intake of sweets, dry mouth, frequency of daily tooth brushing, tremor, the Clinical Global Impression-Schizophrenia Overall severity score, and the Cumulative Illness Rating Scale for Geriatrics score. RESULTS: 523 patients were included in this study (mean ± SD age = 55.6 ± 13.4 years; 297 men). A univariate general linear model showed significant effects of age group, smoking, frequency of daily tooth brushing, and tremor (all p's < 0.001) on the DMFT score (Corrected Model: F(23, 483) = 3.55, p < 0.001, R2 = 0.42) . In other words, older age, smoking, tremor burden, and less frequent tooth brushing were associated with a greater DMFT score. CONCLUSIONS: Given that poor dental condition has been related with an increased risk of physical co-morbidities, physicians should be aware of patients' dental status, especially for aged smoking patients with schizophrenia. Furthermore, for schizophrenia patients who do not regularly brush their teeth or who exhibit tremor, it may be advisable for caregivers to encourage and help them to perform tooth brushing more frequently.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".