Knowledge and Awareness of Dentists about Hypochlorite Emergencies and their Management during Endodontic Treatment
Bibliographic record
Abstract
Objective: This study aims to evaluate and compare dentists' knowledge and awareness of hypochlorite emergencies during endodontic treatment and their management. Study Design: Cross-sectional study. Settings: de ’Montmorency College of Dentistry, Lahore Pakistan. Duration: From July 2023 to January 2024. Methods: The study involved 206 dentists from various dental institutes in Punjab, Pakistan. A meticulously self-developed 21-item questionnaire assessed demographics, knowledge, and awareness of hypochlorite accidents. Data were analyzed using SPSS version 25, with non-parametric tests applied due to non-normal data distribution. Results: Female participants demonstrated significantly higher knowledge scores than males (p=0.012). No significant differences in knowledge scores were found between participants from public and private institutes (p=0.859). Assistant Professors and above had the highest mean knowledge scores (p<0.001). Participants taught about hypochlorite emergencies scored significantly higher (p<0.001). Irrigation beyond the apex was the most frequently reported emergency, particularly among those with more than five years of practice (p=0.004). Conclusion: The study highlights the need for comprehensive training and continuous education to manage hypochlorite emergencies effectively. Addressing knowledge gaps, especially in public institutes, and ensuring the availability of emergency kits can significantly improve patient safety and outcomes. Future research should focus on developing standardized training modules and assessing the long-term impact of enhanced educational 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.004 | 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".