MétaCan
Menu
Back to cohort
Record W2106137871 · doi:10.1191/026921501680425289

Regional variations in stroke care in England, Wales and Northern Ireland: results from the National Sentinel Audit of Stroke

2001· article· en· W2106137871 on OpenAlexaff
Anthony Rudd, P. Irwin, Z Rutledge, D. Lowe, Derick T Wade, M Pearson

Bibliographic record

VenueClinical Rehabilitation · 2001
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsSt. Thomas Hospital
Fundersnot available
KeywordsNorthern irelandAuditStroke (engine)InstitutionalisationCase mix indexMedicineHealth careDemographyRegional variationEmergency medicineNursingAccountingBusinessPolitical scienceSociologyPsychiatry

Abstract

fetched live from OpenAlex

STUDY OBJECTIVE: To identify the variations between regions in England, Wales and Northern Ireland in the case-mix, organization and process of care for stroke. DESIGN: Retrospective audit of case notes and service organization. SETTING: Two hundred and ten Trust sites from 197 Trusts in 10 Health Regions in England, Wales and Northern Ireland. PATIENTS: The 6894 consecutive stroke patients admitted between 1 January and 31 March 1998 (up to 40 per Trust). Audit tool: The Intercollegiate Stroke Audit. RESULTS: There are significant differences in stroke care between regions that cannot be explained by known case-mix or clinical variables. The proportion of patients spending more than half their hospital stay in stroke unit care varied between regions from 10% to 27%. Thirty-day mortality in different regions ranged between 21% and 33%. Institutionalization rates for those admitted from home varied between 6% and 19%. Similar variations existed in discharge disability and length of stay. CONCLUSIONS: There were widespread variations in specialist service provision for stroke in different regions. Regional variation in 30-day mortality and in institutionalization after stroke is not explained by clinical factors and therefore may represent different local health care policies and expectations.

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)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.033
GPT teacher head0.337
Teacher spread0.304 · 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

Citations54
Published2001
Admission routes1
Has abstractyes

Explore more

Same venueClinical RehabilitationSame topicAcute Ischemic Stroke ManagementFrench-language works237,207