MétaCan
Menu
Back to cohort
Record W4386402315 · doi:10.1007/s43678-023-00579-y

Pain management in adult patients with sickle cell disease in the emergency department: how does current practice compare with existing standards of care?

2023· article· en· W4386402315 on OpenAlexafffund
Markus Gulilat, Lanre Tunji-Ajayi, Serena Thompson, Marie-Pascale Poku, Ruth Appiah-Boateng, Nia Navarro, Hasan Sheikh, Jennifer Hulme, Jennifer Bryan, Kaitlin Azubuike, Ulysse Guerrier, Lance Armstrong, Karen Fleming, Tricia S. Williams, Betty Lulseged

Bibliographic record

VenueCanadian Journal of Emergency Medicine · 2023
Typearticle
Languageen
FieldMedicine
TopicHemoglobinopathies and Related Disorders
Canadian institutionsUniversity Health NetworkUniversity of Toronto
FundersUniversity Health Network
KeywordsMedicineTriageEmergency departmentOpioidObservational studyEmergency medicineRetrospective cohort studyVaso-occlusive crisisDiseaseIntensive care medicineInternal medicineSickle cell anemiaPsychiatry

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.068
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.990
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.068
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0050.003
Open science0.0020.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.022
GPT teacher head0.305
Teacher spread0.282 · 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.

Study designObservational
DomainMethods
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

Citations5
Published2023
Admission routes2
Has abstractno

Explore more

Same venueCanadian Journal of Emergency MedicineSame topicHemoglobinopathies and Related DisordersFrench-language works237,207