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Record W2767544582 · doi:10.1161/str.48.suppl_1.tp325

Abstract TP325: Lack of Early Improvement Predicts Poor Clinical Outcome Following Acute Intracerebral Hemorrhage

2017· article· en· W2767544582 on OpenAlexaff
Vignan Yogendrakumar, Eric E. Smith, Andrew M. Demchuk, Richard I. Aviv, David Rodríguez‐Luna, Carlos A. Molina, Imanuel Dzialowski, Adam Kobayashi, Jean-Martin Boulanger, Cheemun Lum, Gord Gubitz, Vasantha Padma, Jayanta Roy, Carlos S. Kase, Rohit Bhatia, Myzoon Ali, Patrick D. Lyden, Michael D. Hill, Dar Dowlatshahi

Bibliographic record

VenueStroke · 2017
Typearticle
Languageen
FieldMedicine
TopicIntracerebral and Subarachnoid Hemorrhage Research
Canadian institutionsDartmouth General HospitalHôpital Charles-Le MoyneSunnybrook HospitalOntario Brain Institute
Fundersnot available
KeywordsMedicineReceiver operating characteristicYouden's J statisticLogistic regressionCutoffCohortInternal medicineOutcome (game theory)Intracerebral hemorrhageArea under the curveSubarachnoid hemorrhage

Abstract

fetched live from OpenAlex

Background: Early Neurological Worsening (ENW) is common after ICH, and predicts poor outcome. However, there is limited data as to what degree of ENW best relates to outcome. We used two ICH cohorts to refine and validate a definition of ENW that best predicted 90-day outcomes. Methods: We generated receiver operating characteristic (ROC) curves for the association between 24-hour NIHSS change and ICH outcomes using data from the VISTA collaboration. Primary outcome was poor outcome at 90 days (mRS 4-6); secondary outcomes were other mRS cutpoints (mRS 2-6, 3-6, 5-6, 6). We tested the commonly used NIHSS≥4 definition and in addition employed Youden’s J Index to select optimal cutpoints and calculated sensitivity, specificity, and predictive values. Independent predictors of poor outcome were determined via multivariable logistic regression. Definitions were validated in the prospectively collected PREDICT-ICH cohort. Results: Using 552 patients from the VISTA cohort, ROC curves of 24hr NIHSS change had an area under the curve of 0.75. NIHSS change of ≥0 at 24hrs was seen in 46.4%. Youden’s method showed an optimum cutoff at -0.5. Based on this, ENW defined as >0 (Sens 43%, Spec 91%, PPV 83%, aOR 7.13 [CI:4.05-12.55]), ≥0 (Sens 65%, Spec 73%, PPV 70%, aOR 5.05 [CI:3.25-7.85]), or ≥-1 (Sens 78%, Spec 59%, PPV 65%, aOR 6.04 [CI:3.75-9.71]) all accurately predicted poor outcome. PPV increased with higher NIHSS cutoffs, but at the cost of lower sensitivities. Regression confirmed that all definitions independently predicted outcome at all mRS cutpoints. ENW definitions reproduced well in the validation cohort of 275 patients. Conclusion: All NIHSS cut-offs for ENW predict clinical outcome, regardless of outcome definition. In particular, lack of clinical improvement at 24 hours (i.e. NIHSS is the same or higher) robustly predicted poor outcome, but may not be sufficiently reliable to determine clinical management.

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)0.002
metaresearch head score (Gemma)0.007
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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.082
GPT teacher head0.409
Teacher spread0.327 · 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".

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Citations0
Published2017
Admission routes1
Has abstractyes

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