Emergency Medicine Research in Pakistan: Trends of publication and quality of evidence
Bibliographic record
Abstract
Abstract Background Emergency medicine has transitioned from developing to developed speciality in Pakistan. There is a need of study to provide insights into areas where most of the research has been done and to understand the areas where research initiatives needs to be directed. Through an in-depth analysis of the current literature, this study aims to identify gaps in emergency medicine research and to suggests area of deficiencies where further studies are required.Methods Emergency medicine related keywords and terms were searched to identify studies published from January 2012 to December 2021 (10 years) in online databases of PubMed, CINAHL, Cochrane and EBSCO. These studies were then filtered through our eligibility criteria. Author, year, study design/ article type, area of study / theme and quality of study were reported along with risk of bias and confounders.Results 150 articles were included in the review out of 871 following the eligibility criteria.Majority of the articles were published in the year 2020 (22.7%) followed by 2016 and 2017 (12% collectively) while the least studies were published in the year 2021 (1.3%). In the same time period, the most common study design was cross-sectional (40.7%) and the least common was mix-method/qualitative (1.3%) whereas, on account of risk of bias, 48% are rated moderately biased while 50% of the included articles had a strong (50%) risk of confounder, yet overall, in around 80% the quality of the paper is marked as predominantly moderate. The leading theme in the cross sectional and cohort studies and case reports was Infectious Diseases, whereas one mix-method study pertaining to Violence and trauma was published in 2021 and one qualitative study in 2015 was related to ethics.Conclusion Analyzing the types and themes of publications and assessing their quality will enable future researchers to focus on lesser explored topics or study designs in their respective areas of interest within emergency medicine.
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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 | 0.056 | 0.274 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.039 | 0.065 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".