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Record W2172028347 · doi:10.1186/s13023-014-0115-7

Targeting under-diagnosis in hereditary hemorrhagic telangiectasia: a model approach for rare diseases?

2014· article· en· W2172028347 on OpenAlexaffabout
Giuseppe A. Latino, Dale Brown, Richard H. Glazier, Jonathan T. Weyman, Marie E. Faughnan

Bibliographic record

VenueOrphanet Journal of Rare Diseases · 2014
Typearticle
Languageen
FieldMedicine
TopicVascular Anomalies and Treatments
Canadian institutionsInstitute for Clinical Evaluative SciencesUniversity of TorontoUniversity Health NetworkPrincess Margaret Cancer CentreHospital for Sick ChildrenSt. Michael's Hospital
FundersHHT Foundation International
KeywordsMedicineTelangiectasiaPediatricsPopulationPrevalenceDemographyCensusEpidemiologyYoung adultNoseGerontologySurgeryEnvironmental healthInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Hereditary hemorrhagic telangiectasia (HHT), a rare autosomal dominant disease, is considered under-diagnosed. Our primary objective was to provide evidence of under-diagnosis of HHT in a North American population. We hypothesized that variation would exist in the diagnosed prevalence (D-prevalence) across regions in the province of Ontario, Canada and across age groups, due to under-diagnosis in certain groups. Our secondary objective was to collect data regarding contact and local access to consult specialists by HHT patients to help guide potential future diagnostic programs. METHODS: Primary objective- 556 adult patients with a definite HHT diagnosis seen at the Toronto HHT Centre were identified and geocoded with postal codes. Prevalence rates were calculated using Canadian census data. Secondary objective- A driving network model was developed in ArcGIS. Service area buffers around ear, nose and throat (ENT) clinics in Ontario were generated to evaluate the proportion of the Ontario population with access to these clinics. A survey was also sent to the email contact list of HHT Foundation International, targeting people with diagnosed HHT, regarding consultation with ENT physicians for epistaxis and timing of HHT diagnosis. RESULTS: Primary objective- D-prevalence rates varied among regions, from no cases to 1.1 cases per 5000 in large Ontario cities. There were no significant differences between urban and rural prevalence rates. Variation in prevalence was seen across age groups, with greater prevalence in older adults (≥50 years-old) compared with adults 20-49 years-old (0.36 versus 0.26 per 5000, p < 0.0005). Secondary objective- Most Ontarians had access to ENT clinics within a 30, 60 and 90 minute modeled drive time (92.7%, 97.8% and 98.6%, respectively). Nearly 40% of surveyed patients consulted an ENT physician for their epistaxis, on average 13.9 ± 12.2 years prior to being diagnosed with HHT. CONCLUSIONS: The prevalence of HHT in Ontario is highly variable across regions and age-groups, suggesting under-diagnosis. Given that patients with HHT frequently consult ENT physicians for epistaxis prior to HHT diagnosis, and that there is almost universal access to ENTs in Ontario, we propose targeting ENT clinics as a province-wide approach to detect undiagnosed HHT patients and families.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.217
Threshold uncertainty score0.431

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.029
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0030.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.017
GPT teacher head0.258
Teacher spread0.241 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations19
Published2014
Admission routes2
Has abstractyes

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