A Cross-Sectional Assessment of Effects of Imprisonment Period on the Oral Health Status of Inmates in Ghaziabad, Delhi National Capital Region, India
Bibliographic record
Abstract
BACKGROUND: Incarcerated individuals usually exhibit high oral health needs than the average population. Several factors contribute to these needs both before incarceration and during the sentence itself. Inmates are a marginalized group, who are at a higher risk for a variety of medical, dental, and emotional disorders than the general population. The aim of the study was to assess of effects of the imprisonment period on the oral health status of inmates. MATERIAL AND METHODS: A total of 532 inmates with imprisonment up to three years, three to six years, and six to ten years were included in the study. Incidence and prevalence of dental caries, decayed, missing, filled teeth (DMFT) index, and periodontal and prosthetic status were evaluated in detail. RESULTS: Results showed that the prevalence of dental caries was relatively high among the convicts. It was found that 98.2% of the inmates had one or more teeth decayed. Additionally, 31.2% (pocket >4mm) of the inmates had poor periodontal status with 4.5% of the subjects having a loss of attachment score of 4-5mm or more. A total of 3.5% of the inmates had full dentures, either upper or lower arch. The relative need for full prosthesis was projected to be around 1.4% of the studied population. CONCLUSION: Within the limitations of the study, the authors found that dental healthcare delivered and received by the inmates is much below the acceptable limit. Additionally, the incidence of dental caries in inmates was unexpectedly higher with tooth decay in 98.2% of subjects. Hence, the need of the hour is to critically incorporate and reinforce our efforts with a special focus on the risk factors of oral health.
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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 | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".