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Record W4205176494 · doi:10.6000/1929-6029

International Journal of Statistics in Medical Research

2023· paratext· en· W4205176494 on OpenAlexvenueno aff

Bibliographic record

VenueInternational Journal of Statistics in Medical Research · 2023
Typeparatext
Languageen
FieldMathematics
TopicStatistical Methods in Epidemiology
Canadian institutionsnot available
Fundersnot available
KeywordsStatisticsMedical statisticsMathematics

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.017
metaresearch head score (Gemma)0.096
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.055
Threshold uncertainty score0.185

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.096
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0080.011
Science and technology studies0.0020.005
Scholarly communication0.0110.005
Open science0.0010.004
Research integrity0.0040.009
Insufficient payload (model declined to judge)0.0550.030

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.

Opus teacher head0.456
GPT teacher head0.669
Teacher spread0.214 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations18
Published2023
Admission routes1
Has abstractyes

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