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Record W7117354554 · doi:10.4103/cmrp.cmrp_20_25

Basic medical statistics for surgeons

2025· article· en· W7117354554 on OpenAlexaff
Neha Sharma, Subhashish Das, Samrat Ray, Shreyas Joshi, S. Nundy

Bibliographic record

VenueCurrent Medicine Research and Practice · 2025
Typearticle
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsToronto General Hospital
Fundersnot available
KeywordsInterpretation (philosophy)Medical statisticsStatistical analysisMedical literatureMedical practice

Abstract

fetched live from OpenAlex

ABSTRACT Biostatistics is an integral part of a surgeon’s practice. From prescribing the correct drugs to planning the proper procedure, an evidence-based approach must be followed. However, not all available evidence from the literature is clinically relevant, and its interpretation requires caution. This interpretation is possible only with a basic understanding of the various statistical terms that enable the critical analysis of the study results. This review aimed to provide a comprehensive overview of frequently used statistical terms and their significance for academic surgeons.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.359
metaresearch head score (Gemma)0.816
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.604
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.3590.816
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0100.000

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 head0.931
GPT teacher head0.739
Teacher spread0.193 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreMethods

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
Published2025
Admission routes1
Has abstractyes

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