The Ability of Oral & Maxillofacial Surgeons to Perform Basic Life Resuscitation in Chattisgarh
Bibliographic record
Abstract
AIM AND OBJECTIVE: This study was conducted to assess the ability of oral & maxillofacial surgeons regarding basic life resuscitation in case of medical emergencies. MATERIALS AND METHODS: This cross-sectional study was conducted among oral & maxillofacial surgeons through a mailed questionnaire. The sample size was finalized to 152 including 108 - males and 44 - females with mean age of the subjects as 30.65 y. The Statistical software namely SPSS version 16.0 was used for data analysis. STATISTICAL ANALYSIS: The student's t-test, ANOVA test and post-hoc test were used as tests of significance for statistical evaluation at p ≤ 0.05. RESULTS: The study revealed that most of the participants were aware about the administration of drugs. Around half of the surgeons (52.4%) were able to understand correct reading of ECG. It has been seen that, 66.8% were properly knowing, how to maintain the airway and 77.4% were experienced in the administration of oxygen in case of emergencies. Overall the knowledge was more among experienced dental surgeons. CONCLUSION: It was found that most of the participants were aware to handle the medical emergencies in dental practice and the awareness was higher among senior surgeons. Still the surgeon should have more knowledge for initial stabilization in a patient with risk happening at dental office.
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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 | 0.000 |
| 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) | 0.005 | 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".