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Record W2907623174 · doi:10.1111/anae.14571

The weekend effect in status epilepticus: a national cohort study

2019· article· en· W2907623174 on OpenAlexaff
Robert Goulden, Tony Whitehouse, Nick Murphy, Thomas Hayton, Zahid Khan, Murali Shyamsundar, Catherine Snelson, Julian Bion, Tonny Veenith

Bibliographic record

VenueAnaesthesia · 2019
Typearticle
Languageen
FieldMedicine
TopicHospital Admissions and Outcomes
Canadian institutionsInstitute of Infection and ImmunityMcGill UniversityMcGill University Health Centre
FundersEpilepsy Research UKNational Institute for Health and Care ResearchQueen Elizabeth Hospital Birmingham Charity
KeywordsWeekend effectMedicineLogistic regressionOdds ratioStatus epilepticusOddsEmergency medicineHospital admissionCohort studyPediatricsInternal medicineEpilepsyPsychiatry

Abstract

fetched live from OpenAlex

Higher mortality following admission to hospital at the weekend has been reported for several conditions. It is unclear whether this variation is due to differences in patients or their care. Status epilepticus mandates hospital admission and usually critical care: its study might provide new insights into the nature of any weekend effect. We studied 20,922 adults admitted to UK critical care with status epilepticus from 2010 to 2015. We used multiple logistic regression to evaluate the association between weekend admission and in-hospital mortality, comparing university hospitals with other hospitals. There were 2462 in-hospital deaths (12%). There was no difference in mortality after weekend admission to university hospitals, adjusted odds ratio (95%CI) 0.99 (0.84-1.16), p = 0.89. Mortality was less after weekend admission than after admissions Monday to Friday in hospitals not associated with a university, adjusted odds ratio (95%CI) 0.74 (0.64-0.87), p = 0.0001. There is no evidence that adults admitted to UK critical care at the weekend in status epilepticus are more likely to die than similar patients admitted during the week.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.009
Threshold uncertainty score0.508

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.006
GPT teacher head0.277
Teacher spread0.271 · 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.

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
Published2019
Admission routes1
Has abstractyes

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