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Record W3033945073 · doi:10.1111/hae.14032

Critical appraisal of the International Prophylaxis Study Group magnetic resonance image scale for evaluating haemophilic arthropathy

2020· review· en· W3033945073 on OpenAlexaff
Mirkamal Tolend, Haris Majeed, Magdy Soliman, Paulo Daruge, Marcelo Bordalo Rodrigues, Sérgio San Juan Dertkigil, Sridhar Gibikote, Shyamkumar N. Keshava, Jennifer Stimec, Amy L. Dunn, Yingjia Li, Victor S. Blanchette, Björn Lundin, Andréa S. Doria

Bibliographic record

VenueHaemophilia · 2020
Typereview
Languageen
FieldMedicine
TopicHemophilia Treatment and Research
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsMedicineMagnetic resonance imagingCritical appraisalArthropathyScale (ratio)SurgeryRadiologyAlternative medicinePathologyOsteoarthritis

Abstract

fetched live from OpenAlex

A goal of the International Prophylaxis Study Group (IPSG) is to provide an accurate instrument to measure MRI-based disease severity of haemophilic arthropathy at various time points, so that longitudinal changes in disease severity can be identified to support decisions on treatment management. We review and discuss in this paper the evaluative purpose of the IPSG MRI scale in relation to its development and validation processes so far. We also critically appraise the validity, reliability and responsiveness of using the IPSG MRI scale in different clinical and research settings, and whenever applicable, compare these clinimetric properties of the IPSG MRI scale with those of its precursors, the compatible additive and progressive MRI scales.

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)0.020
metaresearch head score (Gemma)0.048
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.980
Threshold uncertainty score0.108

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.048
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0050.004
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.089
GPT teacher head0.438
Teacher spread0.349 · 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.

Study designNot applicable
DomainMethods
GenreReview

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

Citations16
Published2020
Admission routes1
Has abstractyes

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