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Record W2969038090 · doi:10.18325/jkmr.2019.29.3.113

A Pilot Study to Evaluate the Reliability of Pattern Identification Tool for Knee Osteoarthritis and to Analyze Correlation between Pattern Identification Tool and Knee Range of Motion, Visual Analog Scale and Western Ontario & Mcmaster Universities Osteoarthritis Index

2019· article· en· W2969038090 on OpenAlexaboutno aff
Seungjoon Oh, Eun-Su Jang, Young-Seon Oh, Weechang Kang, Eunjung Lee, In Chul Jung

Bibliographic record

VenueJournal of Korean Medicine Rehabilitation · 2019
Typearticle
Languageen
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsnot available
FundersKorea Health Industry Development InstituteMinistry of Health and Welfare
KeywordsMedicineIdentification (biology)Reliability (semiconductor)OsteoarthritisRange of motionCorrelationScale (ratio)Visual analogue scalePhysical medicine and rehabilitationPhysical therapyArtificial intelligenceCartographyPathologyComputer scienceMathematicsAlternative medicine

Abstract

fetched live from OpenAlex

Seung-Joon Oh, K.M.D.*, Eunsu Jang, K.M.D., Ph.D., Young-Seon Oh, M.D., Ph.D., Wee-chang Kang, Ph.D., Eun-Jung Lee, K.M.D., Ph.D., In Chul Jung, K.M.D., Ph.D.. J Korean Med Rehabi 2019;29:113-28. https://doi.org/10.18325/jkmr.2019.29.3.113

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)0.007
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.014
GPT teacher head0.282
Teacher spread0.268 · 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 designObservational
Domainnot available
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

Citations2
Published2019
Admission routes1
Has abstractyes

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