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Compatible scales for progressive and additive MRI assessments of haemophilic arthropathy

2005· article· en· W2011959110 on OpenAlexaff
Björn Lundin, Paul Babyn, Andréa S. Doria, Ray F. Kilcoyne, Rolf Ljung, Scott E. Miller, Rachelle Nuss, Georges‐Étienne Rivard, H. Pettersson

Bibliographic record

VenueHaemophilia · 2005
Typearticle
Languageen
FieldMedicine
TopicHemophilia Treatment and Research
Canadian institutionsCentre Hospitalier Universitaire Sainte-JustineHospital for Sick Children
Fundersnot available
KeywordsMedicineMagnetic resonance imagingArthropathyScale (ratio)RadiologyOsteoarthritisPathology

Abstract

fetched live from OpenAlex

The international MRI expert subgroup of the International Prophylaxis Study Group (IPSG) has developed a consensus for magnetic resonance imaging (MRI) scales for assessment of haemophilic arthropathy. A MRI scoring scheme including a 10 step progressive scale and a 20 step additive scale with identical definitions of mutual steps is presented. Using the progressive scale, effusion/haemarthrosis can correspond to progressive scores of 1, 2, or 3, and synovial hypertrophy and/or haemosiderin deposition to 4, 5, or 6. The progressive score can be 7 or 8 if there are subchondral cysts and/or surface erosions, and it is 9 or 10 if there is loss of cartilage. Using the additive scale, synovial hypertrophy contributes 1-3 points to the additive score and haemosiderin deposition contributes 1 point. For osteochondral changes, 16 statements are evaluated as to whether they are true or false, and each true statement contributes 1 point to the additive score. The use of these two compatible scales for progressive and additive MRI assessments can facilitate international comparison of data and enhance the accumulation of experience on MRI scoring of haemophilic arthropathy.

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)0.017
metaresearch head score (Gemma)0.039
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 score0.017
Threshold uncertainty score0.092

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.039
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0040.002
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0020.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.002

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 head0.037
GPT teacher head0.371
Teacher spread0.334 · 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

Citations101
Published2005
Admission routes1
Has abstractyes

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