Knowledge, Attitude and Practice (KAP) Assessment Survey Regarding Oral Mucormycosis after the Covid-19 Pandemic among Dentists in Tricity (Chandigarh, Panchkula and Mohali)
Bibliographic record
Abstract
Background: Mucormycosis is a rare, rapidly progressing opportunistic fungal infection which came into a sudden limelight during the second wave of COVID-19 in India. Aims and Objective: The present study was conducted to evaluate the knowledge, attitude, and practice assessment of oral mucormycosis among dentists in tri-city (Chandigarh, Panchkula, and Mohali) after COVID-19. Materials and Methods: A cross-sectional, web-based survey was carried out among 150 dentists with a response rate of 87.3%. The survey consisted of 15 questions pertaining to knowledge and attitude, whereas a third section of questions regarding practices based on their encounter of attending mucormycosis patients. To ensure maximum participation, snowball and convenience sampling were utilized, and the results were analyzed by descriptive statistics. Results: Dentists demonstrated a decent knowledge about oral mucormycosis, but there was still a lack of awareness pertaining to reasons for developing mucormycosis after COVID and its correlation with COVID-19’s variant. The study participants revealed coherent opinions about most questions except the diagnostic methods. A very few dentists attended to mucormycosis patients in their clinical practice. Conclusion: Overall, the current work reported the knowledge and clinical experience of the dentists regarding COVID-19-associated mucormycosis and emphasizes on improving the knowledge and awareness of dentists in this area for better management of such cases with diligent diagnostic and therapeutic interventions.
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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 | 0.002 | 0.004 |
| 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 | 0.001 | 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".