Bibliographic record
Abstract
To the Editors: I read the review published by Mbuba et al. (2008) in the recent issue of Epilepsia, and I applaud her interest in the topic of the epilepsy treatment gap in developing countries, as it pertained to all epileptologists across the globe. I was surprised that only a small number of articles from Latin America were included in the review. This probably had to do with the fact that LIteratura Latino-Americana y del Caribe en ciencias de la Salud (LILACS) was not interrogated. LILACS represents the Latin American and Caribbean database of biomedical literature published in the region since 1982, indexing approximately 670 journals, and contains more than 350,000 entries in English, Spanish, and Portuguese. This database can easily be accessed on the web at http://www.bvs.br/php/index.php?lang=en Also, I believe that in order to characterize a review as systematic, it is imperative to be exhaustive in the search of the literature in an attempt to use all resources available. That would include manuscripts not written in English, as systematic reviews are at risk of presenting misleading results if they fail to secure a complete sample of the available eligible studies. “Lost science in the Third World” is a phenomenon well known, as many authors from developing countries publish their work in journals not indexed in MEDLINE for different reasons (Gibbs, 1995). Furthermore, the complete data extraction was performed by only one person, whereas a second person re-extracted data from only a sample of half of the studies instead of all of them. With two or more people participating as guards against errors, and if there is good agreement beyond chance between reviewers, the clinician can have more confidence in the results of the systematic review (Guyatt & Jaeschke, 2008). I confirm that I have read the Journal’s position on issues involved in ethical publication and affirm that this report is consistent with those guidelines. Disclosure: The author declares no conflicts of interest.
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 | 0.182 | 0.574 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.014 | 0.018 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.019 | 0.025 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.025 | 0.024 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".