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Record W2300956547 · doi:10.1111/ejh.12581

Optimising musculoskeletal care for patients with haemophilia

2015· review· en· W2300956547 on OpenAlexaff
Manuel Carção, Pamela Hilliard, Miguel A. Escobar, Luigi Piero Solimeno, Johnny Mahlangu, Elena Santagostino

Bibliographic record

VenueEuropean Journal Of Haematology · 2015
Typereview
Languageen
FieldMedicine
TopicHemophilia Treatment and Research
Canadian institutionsInstitute for Clinical Evaluative SciencesSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
FundersGrifolsNovo NordiskBiogenAmgenPfizerSwedish Orphan BiovitrumCSL Behring
KeywordsMedicineHaemophiliaJoint replacementHaemophilia AArthropathyIntensive care medicinePopulationDiseaseArthroplastySurgeryPhysical therapyInternal medicineOsteoarthritis

Abstract

fetched live from OpenAlex

Despite recent improvements in the quality of care and treatment outcomes for haemophilia, joint disease remains a major concern for patients with and without inhibitors. Most bleeding episodes occur in the musculoskeletal system, and recurrent bleeding may result in progressive joint damage, leading to haemophilic arthropathy. Consequently, early identification and management of musculoskeletal bleeding episodes are important to prevent crippling deformities and dysfunction. Management strategies should aim at optimising joint function by reducing the frequency of, and preventing, joint bleeds. Although prophylactic factor replacement is proven to be effective in reducing bleeding frequency into joints and preserving musculoskeletal function in patients without inhibitors, the role for prophylaxis (with bypassing agents) in patients with inhibitors remains unclear. The available bypassing agents, activated prothrombin complex concentrate and recombinant activated factor VII (rFVIIa), are currently the standard of care for acute bleeding episodes in patients with high-titre inhibitors. These agents also prevent bleeding during elective orthopaedic surgery (EOS) in this patient population. This review discusses published data and uses illustrative cases to describe effective strategies for assessing joint health and maintaining optimal musculoskeletal care, focusing on the use of rFVIIa for haemostatic control in haemarthroses and when EOS is required in patients with inhibitors.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.964
Threshold uncertainty score0.925

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.058
GPT teacher head0.374
Teacher spread0.316 · 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 designNot applicable
Domainnot available
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

Citations18
Published2015
Admission routes1
Has abstractyes

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