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Record W3168067398 · doi:10.1101/2021.05.26.21257212

External validation of a clinical decision rule and neuroimaging rule-out strategy for exclusion of subarachnoid haemorrhage in the emergency department: A prospective observational cohort study

2021· preprint· en· W3168067398 on OpenAlexaboutno aff
Tom Roberts, Robert Hirst, William Hulme, Daniel Horner

Bibliographic record

VenuemedRxiv · 2021
Typepreprint
Languageen
FieldMedicine
TopicTraumatic Brain Injury and Neurovascular Disturbances
Canadian institutionsnot available
FundersRoyal College of Emergency MedicineCollege of Emergency MedicineUniversity of BristolNational Institute for Health and Care Research
KeywordsMedicineNeuroimagingObservational studyLumbar punctureEmergency departmentPopulationClinical prediction ruleSubarachnoid hemorrhageProspective cohort studyIntensive care medicineEmergency medicineCerebrospinal fluidSurgeryInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

Abstract Background The investigation of suspected subarachnoid haemorrhage (SAH) presents a diagnostic dilemma. The limited sensitivity of a negative CT brain scan has historically mandated hospital admission and a lumbar puncture to look for evidence of blood in the cerebrospinal fluid. However, emerging evidence has suggested the sensitivity of clinical decision rules and modern CT imaging protocols within early onset of symptoms, may be sufficient to exclude the diagnosis. Methods A prospective, multi-centre, observational study of consecutive adult patients with acute severe non-traumatic headache presenting to emergency departments. We plan to recruit 9000 patients from over a hundred sites across the UK. The primary outcome is adjudicated SAH as defined by neuroimaging or cerebrospinal fluid findings consistent with the diagnosis. Data will be collected on clinical history, examination findings, phlebotomy and imaging results. All participants will be followed for 28-days to identify SAH and other clinically relevant outcomes using case note review, and later Hospital Episode Statistics. A proportionate opt-out model of consent will be used to maximise patient recruitment and study generalisability. Discussion Whilst there is increasing evidence that early neuroimaging strategies for the diagnosis of SAH are very sensitive, there have been no large studies to confirm this in the UK population. Furthermore, the test characteristics of CT brain beyond 6 hours from onset are not well understood and there is limited biological plausibility for this defined time cutpoint. Finally, the performance of the Ottawa clinical decision rule has shown promise in the Canadian population. However, its performance in the UK has not been studied and there are concerns that due to the low specificity it may result in increased, rather than decreased rate of investigations. This study will therefore aim to assess the test characteristics of both a CT brain up to 24h from presentation and the Ottawa SAH clinical decision rule.

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)0.069
metaresearch head score (Gemma)0.180
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 score0.069
Threshold uncertainty score0.363

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0690.180
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.002
Science and technology studies0.0010.002
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.001

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 head0.106
GPT teacher head0.391
Teacher spread0.285 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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