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Record W2106597354 · doi:10.2214/ajr.184.4.01841047

Statistical Inference for Continuous Variables

2005· article· en· W2106597354 on OpenAlexaffabout
Lawrence Joseph, Caroline Reinhold

Bibliographic record

VenueAmerican Journal of Roentgenology · 2005
Typearticle
Languageen
FieldMathematics
TopicStatistical Methods in Clinical Trials
Canadian institutionsMcGill University Health CentreMcGill UniversityMontreal General Hospital
Fundersnot available
KeywordsMedicineSophisticationInferenceComplement (music)Medical physicsStatistical inferenceSoftwareWeb siteAssociation (psychology)Series (stratigraphy)Data scienceThe InternetArtificial intelligenceWorld Wide WebStatisticsComputer scienceProgramming language

Abstract

fetched live from OpenAlex

This is the 15th in the series designed by the American College of Radiology (ACR), the Canadian Association of Radiologists, and the American Journal of Roentgenology. The series, which will ultimately comprise 22 articles, is designed to progressively educate radiologists in the methodologies of rigorous clinical research, from the most basic principles to a level of considerable sophistication. The articles are intended to complement interactive software that permits the user to work with what he or she has learned, which is available on the ACR Web site (www.acr.org).

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)0.069
metaresearch head score (Gemma)0.305
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: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.069
Threshold uncertainty score0.365

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0690.305
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.004
Bibliometrics0.0040.004
Science and technology studies0.0010.006
Scholarly communication0.0030.004
Open science0.0030.003
Research integrity0.0020.008
Insufficient payload (model declined to judge)0.0140.003

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 head0.280
GPT teacher head0.540
Teacher spread0.260 · 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
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

Citations12
Published2005
Admission routes2
Has abstractyes

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