MétaCan
Menu
← Back to cohort
Record W2527698629 · doi:10.3899/jrheum.160649

Determining Metacarpophalangeal Flexion Angle Tolerance for Reliable Volumetric Joint Space Measurements by High-resolution Peripheral Quantitative Computed Tomography

2016· article· en· W2527698629 on OpenAlexaffvenue
Stephanie Tom, Mark Frayne, Sarah L. Manske, Andrew J. Burghardt, Kathryn S. Stok, Steven K. Boyd, Cheryl Barnabé

Bibliographic record

VenueThe Journal of Rheumatology · 2016
Typearticle
Languageen
FieldMedicine
TopicOrthopedic Surgery and Rehabilitation
Canadian institutionsAlberta Bone and Joint Health InstituteUniversity of CalgaryUniversity of TorontoUniversity of Waterloo
Fundersnot available
KeywordsMedicineMetacarpophalangeal jointQuantitative computed tomographyComputed tomographyPeripheralTomographyHigh resolutionJoint (building)Nuclear medicineOrthodonticsRadiologyAnatomyThumbPathologyInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: The position-dependence of a method to measure the joint space of metacarpophalangeal (MCP) joints using high-resolution peripheral quantitative computed tomography (HR-pQCT) was studied. METHODS: Cadaveric MCP were imaged at 7 flexion angles between 0 and 30 degrees. The variability in reproducibility for mean, minimum, and maximum joint space widths and volume measurements was calculated for increasing degrees of flexion. RESULTS: Root mean square coefficient of variance values were < 5% under 20 degrees of flexion for mean, maximum, and volumetric joint spaces. Values for minimum joint space width were optimized under 10 degrees of flexion. CONCLUSION: MCP joint space measurements should be acquired at < 10 degrees of flexion in longitudinal studies.

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)0.006
metaresearch head score (Gemma)0.017
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.038
GPT teacher head0.281
Teacher spread0.244 · 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

Citations14
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueThe Journal of Rheumatology→Same topicOrthopedic Surgery and Rehabilitation→French-language works237,207→