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Record W4229072238 · doi:10.1111/hae.14535

Health issues in women and girls affected by haemophilia with a focus on nomenclature, heavy menstrual bleeding, and musculoskeletal issues

2022· article· en· W4229072238 on OpenAlexaff
Angela C. Weyand, Robert F. Sidonio, Michelle Sholzberg

Bibliographic record

VenueHaemophilia · 2022
Typearticle
Languageen
FieldMedicine
TopicHemophilia Treatment and Research
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
FundersHemophilia Foundation of MichiganNational Hemophilia Foundation
KeywordsMedicineHaemophiliaMenstrual bleedingHaemophilia APhysical therapyPopulationPediatricsGynecologyEnvironmental health

Abstract

fetched live from OpenAlex

INTRODUCTION: Women and girls affected by haemophilia, including haemophilia carriers (WGH) are at risk of bleeding symptoms that may go unrecognized, including heavy menstrual bleeding (HMB) and musculoskeletal bleeding. Terminology continues to evolve. AIM: To describe the current recommendations for nomenclature surrounding WGH, and the current understanding of HMB, iron deficiency, and musculoskeletal complaints in these patients. METHODS: Literature was reviewed and summarized. RESULTS: With regards to nomenclature, women with factor levels less than 50% should be classified as having haemophilia, while carriers with normal levels should be characterized accordingly to symptomatology. HMB and resultant iron deficiency are common among WGH, have a multitude of downstream effects, and maybe overlooked due to stigma around menstruation. Musculoskeletal bleeding and resultant joint changes are increasingly recognized in this population but do not necessarily correlate with factor levels. CONCLUSION: Although progress has been made in the care of WGH, much work remains to further improve their care.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.326
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.012
GPT teacher head0.307
Teacher spread0.295 · 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

Citations36
Published2022
Admission routes1
Has abstractyes

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