Extracorporeal treatments for isoniazid poisoning : systematic review and recommendations from the EXTRIP workgroup
Bibliographic record
Abstract
Agradecimentos: We would like to acknowledge the valuable help of our dedicated translators, librarian, data extractors, and meeting secretary. Official translators were Alexandra Angulo, Alla Abbott, Anant Vipat, Andreas Betz, Angelina Kovaleva, Denise Gemmellaro, Ewa Brodziuk, Helen Johnson, Junzheng Peng, Marcela Covic, Nathalie Eeckhout, Rosie Finnegan, Salih Topal, and Vilma Etchard. The librarian was Elena Guadagno. Data extractors for EXTRIP-2 included Maria Rif, François Filion, Karine Mardini, Maria Rif, Tudor Botnaru, Elizabeth Koo, and Gabrielle Wilson. The meeting secretary was Brenda Gallant. EXTRIP received support consisting of an unrestricted grant of $60,633 Canadian from the Verdun Research Fund (the institution of Marc Ghannoum) solely for the reimbursement of travel expenses for the in-person guideline meeting and payment to dedicated translators for retrieval and translation of foreign language articles. The funder had no role in study design, data collection, data analysis, data interpretation, or writing of the report. All of the authors had full access to all the data and had final responsibility for the decision to submit for publication
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 | 0.078 | 0.199 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.024 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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".