International Journal of Statistics in Medical Research
Bibliographic record
Abstract
The International Journal of Statistics in Medical Research discuss the quality applications of statistics in Health Care research profession. The objective is to highlight advances in statistical sciences and its application to solve problems of human health and diseases, with the ultimate goal of advancing quality of the public health. The International Journal of Statistics in Medical Research seeks to publish new biostatistician models and methods, new statistical theory, as well as original applications of statistical methods, important practical problems arising from several areas of biostatistics and their applications in the field of public health, pharmacy, medicine, epidemiology, bio-informatics, computational biology, survival analysis, health informatics, biopharmaceutical etc. The journal provides a platform for communication between practitioners, researchers, educators, students and policy makers concerned with the application of statistics within Health Care Profession.
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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 | 0.017 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.055 | 0.030 |
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".