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Record W2900542657 · doi:10.1161/strokeaha.118.022332

Declining Admission and Mortality Rates for Subarachnoid Hemorrhage in Canada Between 2004 and 2015

2018· article· en· W2900542657 on OpenAlexafffundabout
Vivien Kin Yi Chan, Patrice Lindsay, Jessica McQuiggan, Brandon Zagorski, Michael D. Hill, Cian O′Kelly

Bibliographic record

VenueStroke · 2018
Typearticle
Languageen
FieldMedicine
TopicIntracranial Aneurysms: Treatment and Complications
Canadian institutionsUniversity of TorontoHeart and Stroke FoundationUniversity of CalgaryUniversity of Alberta
FundersHeart and Stroke Foundation of Canada
KeywordsMedicineSubarachnoid hemorrhageEmergency medicineStroke (engine)Mortality rateIntracerebral hemorrhageCardiologyAnesthesiaInternal medicine

Abstract

fetched live from OpenAlex

Background and Purpose- The purpose of this study was to assess recent trends in the admission and mortality rates for subarachnoid hemorrhage in Canada. Methods- This retrospective cross-sectional study was based on data retrieved from the Canadian Institute for Health Information for all patients diagnosed with subarachnoid hemorrhage in Canada between 2004 and 2015. Adjusted admission rate, in-hospital mortality rates, and discharge disposition were calculated. Results- A total of 19 765 patients were diagnosed with subarachnoid hemorrhage between 2004 and 2015. The mean age was 58.1 years, and 40.3% were men. The annual hospitalization rate was 6.34 per 100 000 person-years, declining by -0.67% annually. In-hospital mortality rate was 21.5%. Conclusions- The Canadian subarachnoid hemorrhage admission and mortality rates are lower than previously reported, with a declining trend.

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)0.003
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 score0.024
Threshold uncertainty score0.177

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.004
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.028
GPT teacher head0.322
Teacher spread0.293 · 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

Citations58
Published2018
Admission routes3
Has abstractyes

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