Prevalence of Periodontal Diseases in Orlu Local Government Area of Imo State, Nigeria
Bibliographic record
Abstract
There has been a global pandemic of periodontal diseases with a rising prevalence worldwide.In Nigeria, reported cases of rising prevalence have occurred because of poor oral hygiene, and other related factors.This descriptive survey study was aimed at ascertaining the prevalence of periodontal diseases in Orlu Local Government Area of Imo state, Nigeria.The main instrument for data collection was a structured interview questionnaire bordering on biodata, economic status, oral hygiene, as well as dental care assessment.A sample of 500 was randomly selected from the population i.e males and females of ages between 6-55yeras (schooling and working population).Data collected was analyzed using descriptive statistics of frequency, percentage, histograms, and pie chart.Results showed that 470(94%) respondents have had one form of periodontal disease while 30(6%) never had it.Also pain and bleeding gums had 34.6% and 19.8% and as such the commonest pattern of periodontal disease in the populace.The disease was commoner amongst 16-25years age group i.e 128(27.2%)and there was also a male preponderance of 59.4% against the female of 40.6%.Sixmonthly dental check-up, and health education were recommended.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".