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Record W4416093277 · doi:10.3390/encyclopedia5040190

Visual Analogue Scale

2025· article· en· W4416093277 on OpenAlexaff
Malcolm Koo, Shih-Wei Yang

Bibliographic record

VenueEncyclopedia · 2025
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsPublic Health OntarioUniversity of Toronto
Fundersnot available
KeywordsVisual analogue scaleOrdinal ScaleScale (ratio)Ordinal dataMeasure (data warehouse)Point (geometry)Line (geometry)Level of measurement

Abstract

fetched live from OpenAlex

The Visual Analogue Scale (VAS) is a psychometric instrument used in research and clinical studies to measure the intensity of subjective experiences that cannot be objectively quantified using defined biomarkers, such as pain, fatigue, or mood. It typically consists of a 100 mm straight line with descriptive anchors at each end representing the extremes of the sensation (for example, “no pain” at one end and “the most severe pain imaginable” at the other). Respondents indicate their experience by marking a point on the line, and the distance from the lower anchor is measured and recorded as a continuous variable. VAS data can be analyzed using descriptive or inferential statistics, with the ordinal and non-linear properties of the scale requiring careful justification of the statistical methods applied.

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)0.003
metaresearch head score (Gemma)0.026
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: Other · Consensus signal: Other
Teacher disagreement score0.092
Threshold uncertainty score0.307

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.026
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0920.025

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 head0.004
GPT teacher head0.293
Teacher spread0.288 · 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

Citations22
Published2025
Admission routes1
Has abstractyes

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