To Assess Knowledge of Basic Life Support among Dental Graduates in Terms of Handling Medical Emergencies
Bibliographic record
Abstract
Aims and objective: Dentists being part of health care providers often encounter with medical emergencies in their dental office, some are trained to cope with, but a large number of dentists lack knowledge and proper training to handle such emergencies results in serious consequences and medicolegal actions. This study will reflect and encourage knowledge and confidence of dental graduates to deal with such situations. Study design: It was a cross sectional study to asses knowledge and management skills of dental graduates, gained during their study in dental college. Place and duration of study: Study comprised of three months’ time (March 2022 to May 2022) in which data was collected from de’Montmorency college of dentistry and Fatima memorial dental college, Lahore. Materials and methods: A self-designed questionnaire was distributed among 98 dental graduates in Lahore. Questionnaire comprised of eleven questions inquiring about medical emergency encountered in their dental clinic/hospital, Basic Life support(BLS), cardiopulmonary resuscitation(CPR), emergency drugs, and equipment and their proper administration and if they want to attend a BLS course in future. Analyzing of data done by statistical package for social sciences SPSS 20.0. Conclusion: Majority of participants were short of confidence regarding executing the chain of BLS. Some participants were having not enough knowledge of location of chest compressions during CPR. Though a good fraction of participants were aware of emergency drugs and equipment but were not enough confident to use them. Majority were agreeing to attend a proper BLS course to strengthen their knowledge and skills to deal medical emergencies. Keywords: Basic Life Support, Cardiopulmonary Resuscitation, Emergency Drugs, Dental Graduates
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.008 | 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".