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Meeting unmet needs in inhibitor patients

2010· article· en· W2152884962 on OpenAlexaff
Victor S. Blanchette, Marilyn J. Manco‐Johnson

Bibliographic record

VenueHaemophilia · 2010
Typearticle
Languageen
FieldMedicine
TopicHemophilia Treatment and Research
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsMedicineHaemophiliaIntensive care medicineQuality of life (healthcare)DiseasePROTHROMBIN COMPLEXCoagulationSurgeryInternal medicine

Abstract

fetched live from OpenAlex

For patients with haemophilia, the development of inhibitors complicates treatment, and inhibitor patients may thus have a range of unmet needs. Although successful inhibitor eradication will render patients responsive to factor replacement therapy, with potentially beneficial effects on long-term outcomes, this may not always be possible. Physicians treating inhibitor patients should aim to achieve reliable control of bleeding episodes, and the prevention of joint disease should also be a priority. Patients with high-titre inhibitors require therapy with bypassing agents--recombinant activated factor VII (rFVIIa) or a plasma-derived activated prothrombin complex concentrate (pd-APCC)--for the treatment of bleeding. When treating joint haemorrhage in inhibitor patients, both aggressive treatment of intercurrent joint bleeds and prophylaxis should be considered, although evidence is needed as to whether prophylaxis with bypassing agents can significantly delay/prevent the development of osteochondral changes in patients with inhibitors. Despite physicians' best efforts, joint disease may ultimately occur in inhibitor patients, and in such instances optimizing treatment, of both early and late stages, is important. There is no single therapeutic modality for dealing with the various treatment challenges posed by inhibitor patients, but overall goals should be to improve quality of life, with the provision of cost-effective care that aims to maintain physical function.

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)0.000
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 score0.042
Threshold uncertainty score0.461

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.016
GPT teacher head0.289
Teacher spread0.272 · 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

Citations15
Published2010
Admission routes1
Has abstractyes

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