Are Medical Practitioners interested in oral- systemic disease connection? - Assessment of awareness and knowledge among Medical Doctors in Port Harcourt
Bibliographic record
Abstract
Background: Though literatures abound as regards the association between chronic periodontitis and non-communicable diseases through the inflammatory pathway that is common to all, however, there is still a low referral from medical doctors to the dentists for oral care. Methodology: All consenting medical practitioners that attended the 2019 Annual General Meeting of the Nigeria Medical Association in Port Harcourt. Data was collected with self- administered questionnaire and analyzed with Statistical Package for Social Sciences version 20.0. Statistical significance was set at p≤0.05. Results: One hundred and fifty-six medical doctors were recruited with M: F of 1.5:1. 28.9% were specialists, 14.1% have practised for over 30 years and 90.4% claimed a knowledge of oral health. Though, 7 out of 10 participants knew gum disease is a form of periodontal disease, only 1 out of 2 and 1 out of 5 knew that the aetiological factor is dental plaque and gingival bleeding is the first sign respectively. One quarter of participants did not know that cigarette smoking is a risk factor for periodontitis. 4 out of 5 participants will seek the dentist opinion for and 9 out of 10 will refer patients. There was statistical significance between participants knowledge of systemic diseases and cadre as regards COPD, CKD and PLBW. Conclusion: Medical doctors have poor awareness of oral-systemic interactions and are not so knowledgeable about the diseases that can result from them. There is therefore, the need to educate them and emphasize the importance of referring their patients for 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".