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Record W2278423921 · doi:10.1160/th15-08-0664

Practical considerations in choosing a factor VIII prophylaxis regimen: Role of clinical phenotype and trough levels

2016· review· en· W2278423921 on OpenAlexaff
Kathelijn Fischer, Manuel Carção, Elena Santagostino, Marilyn J. Manco‐Johnson, Prasad Mathew, Rolf Ljung

Bibliographic record

VenueThrombosis and Haemostasis · 2016
Typereview
Languageen
FieldMedicine
TopicHemophilia Treatment and Research
Canadian institutionsHospital for Sick Children
FundersBaxalta
KeywordsRegimenMedicineHaemophiliaHaemophilia AIntensive care medicineQuality of life (healthcare)ArthropathyPediatricsSurgeryAlternative medicinePathologyNursingOsteoarthritis

Abstract

fetched live from OpenAlex

Current therapy for haemophilia A is guided by severity of the disease, which in turn is best reflected in patients' endogenous factor VIII activity levels. For patients with severe haemophilia (particularly children), prophylaxis with continuous routine factor replacement has become standard of care in developed countries and is gradually becoming the standard of care in developing countries. The question arises then: what is an appropriate prophylaxis regimen to prevent bleeding events and arthropathy, while also maximizing patient quality of life and taking into consideration the costs of prophylaxis? Should all patients be treated with one standard, fixed prophylaxis regimen, or should prophylaxis be individualised for each patient? If so, what factors need to be considered in choosing the appropriate dose and frequency of factor administration? If prophylaxis is tailored to the individual patient, then patient-related factors (bleeding phenotype, activity profiles, age, joint status) and product-specific factors (half-life of the replacement factor in the individual patient) will determine the choice of regimen, whether it be a fixed-regimen prophylaxis or prophylaxis that is tailored to patient activity and bleeding risk. Regardless of the choice of prophylaxis regimen, for any regimen to be effective, adherence to therapy is key to optimising outcomes.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.991
Threshold uncertainty score0.850

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.391
GPT teacher head0.515
Teacher spread0.124 · 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 designOther design
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

Citations33
Published2016
Admission routes1
Has abstractyes

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