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Sonography for assessment of haemophilic arthropathy in children: a systematic protocol

2007· article· en· W2034789818 on OpenAlexaff
Katherine Zukotynski, José Jarrín, Paul Babyn, Manuel Carção, Janneth Pazmino‐Canizares, A. M. Stain, Andréa S. Doria

Bibliographic record

VenueHaemophilia · 2007
Typearticle
Languageen
FieldMedicine
TopicHemophilia Treatment and Research
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsMedicineHaemophiliaArthropathyHemarthrosisMagnetic resonance imagingJoint effusionRadiologySynovitisSurgeryArthritisOsteoarthritisPathology

Abstract

fetched live from OpenAlex

Radiological imaging of joints in children with haemophilia is important to detect abnormalities, grade their severity and monitor the effects of treatment. Scoring systems for staging haemophilic arthropathy have been developed based on plain film or magnetic resonance imaging (MRI) findings. Radiographs alone may be inadequate for evaluating joint disease in children with haemophilia on prophylaxis while MRI may be difficult to access and require the child to be sedated. Sonography can be a useful complementary modality in the evaluation of haemophilic arthropathy that is readily available and does not require the child to be sedated. In this paper, we briefly review the current imaging scales available for the assessment of haemophilic arthropathy and present a systematic protocol for sonographic assessment of the knee and ankle in haemophilic children along with examples of findings in joint effusion/hemarthrosis, synovial hypertrophy and cartilage loss. Also, we correlate the ultrasound findings with the corresponding MRI images demonstrating the anatomic planes used for imaging acquisition. Sonography is a promising technique for the assessment of soft tissue changes which are the earliest findings in haemophilic arthropathy. Further investigation is required for evaluation of osteochondral changes given limitations of sonography in this regard and in minimizing operator dependency, especially if applied in multicentric clinical trials.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.182
Threshold uncertainty score0.864

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.390
Teacher spread0.352 · 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 teacher head, 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

Citations99
Published2007
Admission routes1
Has abstractyes

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