Bibliographic record
Abstract
Clinical case definitions are the cornerstone of clinical communication and of clinical and epidemiologic research. The ramifications of establishing a case definition are extensive, including potentially large changes in epidemiologic estimates of frequency, and decisions for clinical management. Yet, defining a condition entails numerous challenges such as defining the scope and purpose, incorporating the strongest evidence base with clinical expertise, accounting for patients' values, and considering impact on care. The clinical case definition of drug-resistant epilepsy, in addition, must address what constitutes an adequate intervention for an individual drug, what are the outcomes of relevance, what period of observation is sufficient to determine success or failure, how many medications should be tried, whether seizure frequency should play a role, and what is the role of side effects and tolerability. On the other hand, the principles of evidence-based medicine (EBM) aim at providing a systematic approach to incorporating the best available evidence into the process of clinical decision for individual patients. The case definition of drug-resistant epilepsy proposed by the the International League Against Epilepsy (ILAE) in 2009 is evaluated in terms of the principles of EBM as well as the stated goals of the authors of the definition.
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 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 | 0.036 | 0.126 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.003 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".