Bibliographic record
Abstract
The objective is to illustrate the creation and structure of a particular type of Evidence Based Care (EBC) summary that has direct clinical relevance, the Critically Appraised Topic (CAT). The process consists of a step-by-step application of the EBC principles to a common neurological problem, i.e., a patient presenting with a first, unprovoked generalized seizure. This includes asking a focused clinical question about prognosis for recurrence and the role of antiepileptic drugs; searching the literature to answer the question; selecting the relevant evidence (a meta-analysis about prognosis and a randomized controlled trial about therapy); appraising the literature for its validity and usefulness; and applying the results to the clinical scenario. The result is a one-page, user friendly CAT whose title states a declarative answer to the clinical question. It also contains a description of the literature search and of the evidence, the clinical bottom lines derived from the evidence, and general comments.
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 | 0.053 | 0.119 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.014 | 0.005 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".