Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".