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Record W303532293

Are you listening?

2005· article· en· W303532293 on OpenAlexaboutno aff
Andrew Varadi

Bibliographic record

VenuePubMed · 2005
Typearticle
Languageen
FieldMedicine
TopicOrthopedic Surgery and Rehabilitation
Canadian institutionsnot available
Fundersnot available
KeywordsChiropracticComputer scienceActive listeningGrindJoint (building)CartilageSound (geography)MedicineData scienceMedical physicsAnatomyPsychologyPathologyGeologyAlternative medicineEngineeringMechanical engineeringOceanographyCommunication
DOInot available

Abstract

fetched live from OpenAlex

Many providers listen for the presence of crepitus or grind, but few delve deeper. Sonic cartilage topography and synovial friction assays at the clinical level are innovative and thus hard to reference. The technique was derived over a 20-year period and is traced in a series of articles in the Canadian Chiropractor beginning 1999. The term, ‘Joint Sound Diagnostics (JSD)’ refers to the sensitive tracking and monitoring of post-traumatic and early degenerative joint conditions at the clinical level. Concurrent similarly related studies are also in the works.1,2

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)0.023
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: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.113
Threshold uncertainty score0.378

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.023
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0050.003
Scholarly communication0.0060.007
Open science0.0010.004
Research integrity0.0040.008
Insufficient payload (model declined to judge)0.1130.062

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 head0.022
GPT teacher head0.245
Teacher spread0.223 · 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
GenreCommentary

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
Published2005
Admission routes1
Has abstractyes

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Same venuePubMed→Same topicOrthopedic Surgery and Rehabilitation→French-language works237,207→