Contribution Title Analysis and evolution of mortality and morbity: a review of articles published during 2016–2021
Bibliographic record
Abstract
Morbidity and mortality are two frequent epidemiological monitoring measures. These parameters indicate how a health issue develops and how severe it becomes. They're important for learning about illness risk factors and comparing health events and populations. From 2016 to 2021, 241 bibliographic records were extracted from the Scopus database and evaluated through author, journal, country, and keyword analyses. The United States, United Kingdom, Australia, France, and Canada made the most substantial contributions to the domain. Moreover, the findings revealed that during the study period, the publication of papers relating to mortality and morbity increased, and the United States produced the largest proportion of publications and authors (22 percent of total). The researchers are interested in strongly highly loaded citation. This work also examines the most used input keywords. Accordingly, the major findings of this study will be useful for politicians, researchers, and institutions to determine future research directions and identify potential consultants to assist formulating their mortality and morbity control policies and future mortality reduction objectives
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.020 | 0.020 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".