MétaCan
Menu
Back to cohort
Record W2091019309 · doi:10.1002/sim.5466

Sample size formulas for estimating intraclass correlation coefficients with precision and assurance

2012· article· en· W2091019309 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueStatistics in Medicine · 2012
Typearticle
Languageen
FieldDecision Sciences
TopicReliability and Agreement in Measurement
Canadian institutionsRobarts Clinical TrialsWestern University
Fundersnot available
KeywordsIntraclass correlationConfidence intervalReliability (semiconductor)Sample size determinationStatisticsInterval (graph theory)MathematicsCoverage probabilityLimit (mathematics)Interval estimationSample (material)Correlation coefficientCorrelationComputer scienceReproducibilityPower (physics)Mathematical analysisCombinatoricsPhysics

Abstract

fetched live from OpenAlex

The number of subjects required to estimate the intraclass correlation coefficient in a reliability study has usually been determined on the basis of the expected width of a confidence interval. However, this approach fails to explicitly consider the probability of achieving the desired interval width and may thus provide sample sizes that are too small to have adequate chance of achieving the desired precision. In this paper, we present a method that explicitly incorporates a prespecified probability of achieving the prespecified width or lower limit of a confidence interval. The resultant closed-form formulas are shown to be very accurate.

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.

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)0.007
metaresearch head score (Gemma)0.059
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.575
Threshold uncertainty score0.949

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.059
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.085
GPT teacher head0.396
Teacher spread0.310 · 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