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Record W2074090246 · doi:10.3109/03630269.2014.954048

Adult Sickle Cell Disease Epidemiology and the Potential Role of a Multidisciplinary Comprehensive Care Center in a City with Low Prevalence

2014· article· en· W2074090246 on OpenAlexaffabout
Andrew Binding, Karen Valentine, Man‐Chiu Poon, Farzana Sayani

Bibliographic record

VenueHemoglobin · 2014
Typearticle
Languageen
FieldMedicine
TopicHemoglobinopathies and Related Disorders
Canadian institutionsUniversity of CalgaryMcGill University
Fundersnot available
KeywordsMedicineEmergency departmentEpidemiologyDiseaseSickle cell anemiaPediatricsRetrospective cohort studyPopulationEmergency medicineInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

The aim of this study was to determine the characteristics of the sickle cell disease population in a city of low prevalence and compare them to those reported in the literature. We performed a retrospective cross-sectional study of all sickle cell disease patients seen in the Calgary Health Region, Calgary, Alberta, Canada from 2006 to 2010. Data on clinical endpoints including emergency department (ED) visits, hospital admissions, transfusions, as well as laboratory parameters were collected. A total of 37 adult sickle cell disease patients were identified. Over 5 years, they were represented by a total of 49.2 ED presentations/year, 29.2 (59.0%) of these requiring admission. Eighty-three percent of these presentations were for acute pain episodes. We concluded that the number of ED visits, hospital admissions and several other parameters in our cohort were similar to those in other centers of higher prevalence. This suggests that guidelines representing regions of high prevalence may be applicable to smaller centers, where patients experience similar clinical outcomes.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.090
Threshold uncertainty score0.179

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.005
GPT teacher head0.231
Teacher spread0.227 · 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 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

Citations2
Published2014
Admission routes2
Has abstractyes

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