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Record W4367679921 · doi:10.1101/2023.04.25.23289111

Spike ripples localize the epileptogenic zone better than other leading biomarkers: a multicenter intracranial study

2023· preprint· en· W4367679921 on OpenAlexaff
Wen Shi, Dana Shaw, Katherine G. Walsh, Xue Han, Uri T. Eden, R. Mark Richardson, S. Gliske, Julia Jacobs, Benjamin H. Brinkmann, Gregory A. Worrell, William C. Stacey, Mark Kramer, Catherine J. Chu

Bibliographic record

VenuemedRxiv · 2023
Typepreprint
Languageen
FieldMedicine
TopicEpilepsy research and treatment
Canadian institutionsAlberta Children's HospitalUniversity of Calgary
FundersNational Institute of Neurological Disorders and StrokeNational Institutes of Health
KeywordsSpike (software development)MedicineRippleResectionAudiologySurgeryPhysicsComputer science

Abstract

fetched live from OpenAlex

Abstract Objective We evaluated whether the combination of epileptiform spikes and ripples (spike ripples) outperformed other leading biomarkers in identifying the epileptogenic zone across subjects in a multicenter international study. Methods We validated and applied an automated spike ripple detector on intracranial EEG recordings in subjects from 4 centers who subsequently underwent surgical resection with known 1-year seizure outcomes. We evaluated the spike ripple rate in subjects cured after resection (ILAE 1 outcome) and those with persistent seizures (ILAE 2-5) across sites and recording types. We also evaluated spike, wideband HFO (80-500 Hz), fast ripple (250-500 Hz), and ripple (80-250 Hz) rates using validated automated detectors. The proportion of resected events was computed and compared across subject outcomes and biomarkers. Results 109 subjects were included. The majority of spike ripples were removed in subjects with ILAE 1 outcome (p = 1e-6), and this was qualitatively observed across the four sites (p = 0.032, p = 0.092, p = 0.0005, p = 0.003) and the two electrode types (p = 0.01, p = 7e-6). A higher proportion of spike ripples were removed in subjects with ILAE 1 outcomes compared to ILAE 2-5 outcomes (p = 0.02). Among ILAE 1 subjects, the proportion of spike ripples removed was higher than the proportion of spikes (p = 0.0004), wideband HFOs (p = 0.0004), fast ripples (p = 0.008), and ripples (p = 0.008) removed. At the individual level, more subjects with ILAE 1 outcome had the majority of spike ripples removed (40/48, 83%) than spikes (69%, p = 0.04), wideband HFOs (63%, p = 0.009), fast ripples (36%, p = 2e-5), or ripples (45%, p = 0.0007) removed. Interpretation When surgical resection was successful, the majority of spike ripples were removed. Automatically detected spike ripples have improved specificity for epileptogenic tissue compared to spikes, wideband HFOs, fast ripples, and ripples.

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)0.003
metaresearch head score (Gemma)0.005
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 score0.003
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.059
GPT teacher head0.342
Teacher spread0.283 · 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
Published2023
Admission routes1
Has abstractyes

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