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Natural history of complications after intracerebral haemorrhage

2009· article· en· W1998476354 on OpenAlexaff
Myzoon Ali, Patrick D. Lyden, Ralph L. Sacco, Ashfaq Shuaib, Kennedy R. Lees

Bibliographic record

VenueEuropean Journal of Neurology · 2009
Typearticle
Languageen
FieldMedicine
TopicIntracerebral and Subarachnoid Hemorrhage Research
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineStroke (engine)ComplicationLogistic regressionClinical trialPopulationPlaceboRandomizationOdds ratioSurgeryIntensive care medicineInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND AND PURPOSE: Numerous trials of haemostatic and neuroprotective agents for intracerebral haemorrhage (ICH) have failed. We characterized the risk of complications after ICH in a trial-eligible patient population, to inform safety in future trials. METHODS: We used the Virtual International Stroke Trials Archive database to identify placebo-treated patients with spontaneous ICH, who were not comatose at admission, where randomization took place within 4 h of symptom onset, and where serious complication and outcome data were available. We described the complications encountered and assessed whether the absence of common complications influenced attainment of good functional outcome (mRS < or =4) at 90 days using logistic regression. RESULTS: Of 201 patients examined, 70.2% experienced at least one serious complication. Neurological complications occurred in 21%, infections amongst 11%, and thromboembolic complications in 2%. Extension of the haemorrhage occurred most frequently: its absence was a significant predictor of good functional outcome (P < 0.0001, adjusted OR for good functional outcome = 21.9, 95% CI: [5.5, 88.3]). Neither infection, nor cardiac, nor thromboembolic complications influenced functional outcome at 90 days. CONCLUSIONS: Three month outcome in ICH patients depends on initial stroke severity and on enlargement of the haemorrhage. Our results should inform safety in future clinical trials of putative ICH therapies.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.915
Threshold uncertainty score0.411

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.001
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 head0.019
GPT teacher head0.260
Teacher spread0.241 · 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

Citations28
Published2009
Admission routes1
Has abstractyes

Explore more

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