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Comparing bleed frequency and factor concentrate use between haemophilia A and B patients

2011· article· en· W2111744314 on OpenAlexaff
Kim Nagel, Irwin Walker, Kay Decker, Anthony K.C. Chan, Menaka Pai

Bibliographic record

VenueHaemophilia · 2011
Typearticle
Languageen
FieldMedicine
TopicHemophilia Treatment and Research
Canadian institutionsMcMaster UniversityHamilton Health Sciences
Fundersnot available
KeywordsMedicineBleedHaemophiliaHaemophilia AHaemophilia BPediatricsSurgeryInternal medicine

Abstract

fetched live from OpenAlex

Haemorrhagic manifestations in patients with haemophilia A and B are considered quite similar for comparable level of factor deficiency. We investigated the bleeding frequency and factor usage between HA and HB patients with comparable disease severities. We collected data on frequency of bleeds and factor concentrate utilization over 3 years, from January 2001 to December 2003. Information was gathered from home infusion logs recorded by patients or their parents, and treatment records from the Hemophilia Clinic or the Hospital Emergency Department. Data were available on 58 patients with severe HA (FVIII < 0.01 U mL(-1)), 10 with moderate HA (FVIII < 0.05 U mL(-1)), 15 with severe HB, and five with moderate HB who required treatment for episodic bleeds, postoperative haemostasis and for primary or secondary prophylaxis. The HA patients bled more frequently than HB patients (14.4 vs. 8.63 bleeds/patient/year), but used similar amounts of concentrate per year. HA patients underwent surgical procedures 3.2 times more frequently than HB patients to correct musculoskeletal complications. A total of 21,363,409 IU of recombinant FVIII was used by patients with HA (104,722 IU/patient/year) and 6,430, 960 IU of recombinant factor IX, by patients with HB (107,182 IU/patient/year). The difference in factor concentrate usage is not statistically significant (P > 0.05). The decrease in bleed frequency in haemophilia B indicates that the conclusions from randomized trials of prophylaxis in HA may not be accurately applied to HB.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.147
GPT teacher head0.297
Teacher spread0.150 · 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.

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

Citations65
Published2011
Admission routes1
Has abstractyes

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