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Record W3175038496 · doi:10.17225/jhp00178

Personalising haemophilia management with shared decision making

2021· article· en· W3175038496 on OpenAlexaff
Leonard A. Valentino, Victor S. Blanchette, Claude Négrier, Brian O’Mahony, Val Bias, Thomas Sannié, Mark W. Skinner

Bibliographic record

VenueThe Journal of Haemophilia Practice · 2021
Typearticle
Languageen
FieldMedicine
TopicHemophilia Treatment and Research
Canadian institutionsSickKids FoundationHospital for Sick ChildrenMcMaster UniversityUniversity of Toronto
Fundersnot available
KeywordsHaemophiliaMedicinePaternalismDecision aidsNursingAlternative medicinePediatrics

Abstract

fetched live from OpenAlex

Abstract The current standard of care for treating people with haemophilia (PWH) in the developed world is prophylaxis with regular infusions of clotting factor concentrates. Gene therapy is being investigated as a new treatment paradigm for haemophilia and if approved would potentially eliminate the need for chronic, burdensome infusions. In recent years, shared decision making (SDM) has become increasingly common in patient care settings. SDM is a stepwise process that relies on reciprocal information sharing between the practitioner and patient, resulting in health care decisions stemming from the informed preferences of both parties. SDM represents a departure from the traditional, paternalistic clinical model where the practitioner drives the treatment decision and the patient passively defers to this decision. As the potential introduction of gene therapy in haemophilia may transform the current standard of care, and impact disease management and goals in unique ways, both practitioners and PWH may find their knowledge tested when considering the appropriate use of a novel technology. Therefore, it is incumbent upon haemophilia practitioners to foster an open, trusting, and supportive relationship with their patients, while PWH and their caregivers must be knowledgeable and feel empowered to participate in the decision making process to achieve truly shared treatment decisions.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.520
Threshold uncertainty score0.545

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.035
GPT teacher head0.357
Teacher spread0.321 · 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 designCase report
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

Citations29
Published2021
Admission routes1
Has abstractyes

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