MétaCan
Menu
Back to cohort
Record W2487366513

PLEDs: clinical correlates.

2007· article· en· W2487366513 on OpenAlexaff
W. J. Fitzpatrick, N. Lowry

Bibliographic record

VenuePubMed · 2007
Typearticle
Languageen
FieldMedicine
TopicEpilepsy research and treatment
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsEtiologyMedicineNeuroimagingPediatricsStroke (engine)MigraineInternal medicine
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVE: We reviewed our experience in 96 consecutive patients exhibiting periodic lateralized epileptiform discharges (PLEDs) on EEG. METHODS: EEG reports from January 1, 1999 to September 30, 2006 were screened for the term 'PLEDs' and its variants. A retrospective chart review, including examination of neuroimaging and other investigations, was conducted on each patient identified. RESULTS: Acute stroke, tumor and central nervous system infection were the most common etiologies, accounting for 26%, 12% and 12% of cases respectively. Acute hemorrhage and traumatic brain injury combined accounted for another 12%. Previously unreported etiologies included posterior reversible encephalopathy syndrome (PRES), familial hemiplegic migraine and cerebral amyloidosis. There were 9 cases of chronic PLEDs attributable to underlying cortical dysplasia or severe remote cerebral injury, all with an accompanying partial seizure disorder. A prominent role for alcohol withdrawal was noted, and in 6 cases was the sole etiological factor. Fever was present as a potential contributing factor in 40% of cases, and significant metabolic abnormalities in 35%. Seizure activity occurred in 85% of patients overall, but in 100% of patients with PLEDs Plus and BiPLEDs Plus. The overall mortality rate was 27%. Mortality among patients with BiPLEDs however was almost twice that, at 52%. CONCLUSIONS: This case series demonstrates the wide variety of potential PLED etiologies. It also emphasizes that despite advances in neurocritical care, the morbidity and mortality associated with PLEDs has changed little since their recognition four decades ago.

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 score0.365
Threshold uncertainty score0.372

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 head0.074
GPT teacher head0.363
Teacher spread0.289 · 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

Citations108
Published2007
Admission routes1
Has abstractyes

Explore more

Same venuePubMedSame topicEpilepsy research and treatmentFrench-language works237,207