Bibliographic record
Abstract
This supplement reviews the evidence used to diagnose and treat patients with seizures and epilepsy. Despite the exponentially growing literature on the basic and clinical sciences pertaining to epilepsy, the quality of the scientific evidence available to clinicians remains limited. Diagnosis may be more problematic than we imagine. Paroxysmal spells are typically infrequent; they are rarely witnessed by physicians. Video-EEG documentation of an episode is our diagnostic gold standard, but this tool cannot be applied to the vast majority of patients with infrequent episodes. How accurate are we at diagnosing a brief episode of unresponsiveness in a child? Was it a daydream, an attentional lapse, or a partial or absence seizure? Short of capturing a spell on EEG or video-EEG, what is the reliability and validity of the criteria we use to differentiate these spells? The evidentiary picture remains equally incomplete on therapy. Most antiepileptic drug trials rely on patient and family self-report of seizure frequency. Yet it is well documented that many patients are unaware of their seizures and will underreport them. The rate of overreporting is not well studied, but may be an issue as well. Thus, the major data pool we use to decide about whether a drug or device is effective may be slightly, or modestly, inaccurate. Whether we are deciding on treatment of a patient with partial or generalized epilepsy, the data to compare currently available antiepileptic drugs in their relative efficacy and toxicity are sparse. In the clinic, trying to apply the scientific evidence to the patient we are counseling about therapy, we are left without clear direction and often make selections based on different side effect profiles since the comparative efficacy data provide little guidance to select one drug versus another. Our goals in this supplement are to highlight the available evidence as well as the lack of data for the clinical care of epilepsy patients. The different classes of evidence are, in order of increase value: case reports (6), case series (5), phase IV studies (4), case-control studies (3), cohort studies (2), and randomized clinical trials (RCTs) (1). Most of the RCTs in epilepsy focus on antiepileptic drugs (AEDs), usually comparing the addition of one AED as an adjunctive therapy to the effects of placebo. Yet even well-executed scientific studies that are published in the prestigious journals can fail to be replicated. For example, the finding that a specific polymorphism in the drug-transporter gene ABCB1 (1) was greeted with tremendous excitement that a simple blood test could screen patients to identify responders and nonresponders to certain AEDs. However, two subsequent groups failed to replicate this finding (2,3). Similarly, case reports, the lowliest level of evidence can be the key to the discovery of previously unknown syndromes, therapies, and side effects. Given the limited scientific evidence in clinical epilepsy, we must remain open to all of the available evidence, and be humble, recalling that our limits far exceed our knowledge. This supplement provides reviews on various topics in the diagnosis and management of epilepsy patients. In addition to the traditional articles that review topics, we have also included two case reports and three debates. The case reports highlight diagnostic and therapeutic challenges and quagmires in adult and pediatric epilepsy. The debates provide a novel format to highlight differing approaches to a diagnostic (is the EEG essential?) and therapeutic (do we treat the EEG to burst suppression in nonconvulsive status?) issue. Although debates are traditionally oral, we preserve them in this supplement because they provide an essential missing link in translating the clinical literature to practice—interpretation. Review articles, the traditional academic summaries on a topic, often converge on a mean that represents a mainstream, balanced view. Yet, in practice, many senior clinicians and scientists differ about how to interpret data and implement care. These differences may be as illuminating as the mean. These debates are therefore more of opinion pieces than traditional articles. Dan Mayer provides an overview on evidence-based medicine. Why it is important. There is often a dissociation between the clinicians' recognition that evidence-based medicine is important and their failure to apply it soundly in practice. Siddhu Nadkarni presents the case of a woman with refractory epilepsy who is found, many years into her epilepsy course, to have an episode of a serious cardiac dysrhythmia requiring pacemaker placement. Despite capturing episodes on video-EEG, diagnostic questions about the relationship between the epilepsy and the cardiac arrhythmia remained. Cees Van Donselaar and colleagues review some fundamental questions in epilepsy care: How confident are we of the diagnosis of epilepsy? How common is misdiagnosis? How useful is the EEG in making the diagnosis versus classifying the syndrome? Jyoti Pillai and Michael Sperling examine the role of interictal EEG in the diagnosis of epilepsy. They review the common, and some of the uncommon, pathological and benign variants that populate EEGs. A debate follows on the following topic: Resolved: The EEG is an essential clinical tool. Nathan Fountain argues to pro side and John Freeman the con side. The lively debate highlights some of the reasons EEGs are done commonly in some centers and practices and infrequently in others. Douglas Nordli, Jr. reviews the use and limits of video-EEG in the diagnosis of epilepsy. This gold standard is underutilized but it is also vulnerable and subject to interpretation. Peter and Carol Camfield review the value of AED levels and toxicity screens in the care of epilepsy patients. They provide a perspective that reflects practice throughout most of the world beyond the U.S. borders. It should provoke thought as to whether American physicians obtain too many AED levels and serum toxicity screens in their patients. Daniel Lowenstein critically appraises treatment approaches to refractory status epilepticus. What is the evidence for efficacy and toxicity of the various protocols used at different centers to treat this disorder that carries a high morbidity and mortality? Another debate follows: Resolved: In nonconvlusive status epilepticus, treat the EEG to burst suppression. Kenneth Jordan takes the pro side while Lawrence Hirsch takes the con side. The controversy focuses on how aggressive therapy should be, and what are the costs of such aggressive therapies? Page Pennell provides an up-to-date review of current data on the treatment of women with epilepsy. The availability of data from pregnancy registries and studies that are examining the developmental outcomes of children born to women with epilepsy on various therapies has already begun to influence clinical care. Dennis Dlugos presents the case of a child whose epilepsy initially appeared to be relatively benign but it then evolved to become a refractory and severe case. What early clues suggested this course? What are the treatment options? David Chadwick examines when to start and stop antiepileptic drugs. In addition to reviewing the literature, he shows how sufficiently powered studies can help to develop predictive models that assist decision making. Elinor Ben-Menachem and Jacqueline French examine the value of AED guidelines in managing patients. Guidelines are developed using all of the highest quality data. Yet, paradoxically, if these data are derived from studies that do not mirror clinical practice, how valuable is such data when applied to clinical practice? Ilo Leppik discusses the epidemiology, diagnosis, and treatment of epilepsy in the elderly. This enormous, and often undertreated or overtreated, population presents a special challenge. Warren Blume reviews the progressive nature in epilepsy in some individuals. What is the evidence for progression? Elaine Wirrell examines epilepsy-related injuries. This article provides the most comprehensive and clinically relevant available data on this topic. Finally, Elson So covers the current studies on SUDEP, examining the incidence, risk factors, and potential preventive strategies. We hope this supplement provides the reader with current evidence about a spectrum of clinically relevant topics in epilepsy. Clinical care requires that we take these imperfect data and apply them to individual patients. Understanding the limits and controversies that surround these data can assist in clinicians as well as their patients and family members about clinical decisions that influence care.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.327 | 0.187 |
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".