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Misunderstandings of statistics in medical research

2013· article· en· W3031801336 on OpenAlexaboutno aff
Ce Zhang

Bibliographic record

VenueZhonghua yixue keyan guanli zazhi · 2013
Typearticle
Languageen
FieldMathematics
TopicStatistical Methods in Epidemiology
Canadian institutionsnot available
Fundersnot available
KeywordsStatisticsStatistical hypothesis testingTest (biology)Medical statisticsQuarter (Canadian coin)Key (lock)Chi-square testStatistical analysisComputer scienceData sciencePsychologyMathematicsHistory

Abstract

fetched live from OpenAlex

To explore the misunderstandings of statistics in medical research,we surveyed related papers from CNKI published in the first quarter of 2012 and analysed the statistical application in those papers.Misunderstandings were identified and solutions were proposed to avoid them. Key words: Statistics;  Random Grouping;  t test;  Chi-square test;  Repeated measurement data

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)0.487
metaresearch head score (Gemma)0.740
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: Methods
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.513
Threshold uncertainty score0.632

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.4870.740
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0240.019
Science and technology studies0.0050.023
Scholarly communication0.0160.015
Open science0.0060.009
Research integrity0.0080.014
Insufficient payload (model declined to judge)0.0020.002

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 head0.326
GPT teacher head0.529
Teacher spread0.203 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designObservational
DomainMethods
GenreEmpirical

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

Citations0
Published2013
Admission routes1
Has abstractyes

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