Improvement of Quality and Patient Safety in Anaesthesia Practice in Low-Resource Settings
Bibliographic record
Abstract
In modern healthcare system, health policy makers, healthcare professionals and organizations, and patients are becoming more aware of the importance of promoting quality care and safety practices. In anaesthesia practice, the specialty has come a long way since its establishment in Dhaka Medical College Hospital in Bangladesh in 1950s by the house surgeons at that time. In last few decades, there had been incredible innovations in anaesthesia, such as the introduction of sevoflurane, isoflurane, propofol, the laryngeal mask airway, pulse oximetry, HDU and ICU facilities and many more. However, in Bangladesh, being a low-resource country, there are still many challenges that the specialty must meet especially in quality of care and patient safety culture. Following some recent anaesthesia-related fatalities, concerns about anaesthesia flared up among patients, health professionals and policy makers. Therefore, different healthcare organizations, both public and private, are now looking at the resiliency analysis of the anaesthesia practice in order to improve quality and patient safety to enhance patient satisfaction and trust building. This review paper aims to discuss quality improvement and patient safety in anesthesia practice and their contributing factors, standardized practices, and strategies to facilitate safety with a focus on teamwork and communication in low-resource settings. However, the review does not seek to be exhaustive or systematic, but to highlight current areas of concern and some potential solutions. Mugda Med Coll J. 2024; 7(2): 127-134
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".