The EXTRIP (<i>EXtracorporeal TReatments In Poisoning</i>) workgroup: Guideline methodology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Extracorporeal treatments (ECTRs), such as hemodialysis and hemoperfusion, are used in poisoning despite a lack of controlled human trials demonstrating efficacy. To provide uniform recommendations, the EXTRIP group was formed as an international collaboration among recognized experts from nephrology, clinical toxicology, critical care, or pharmacology and supported by over 30 professional societies. For every poison, the clinical benefit of ECTR is weighed against associated complications, alternative therapies, and costs. Rigorous methodology, using the AGREE instrument, was developed and ratified. Methods rely on evidence appraisal and, in the absence of robust studies, on a thorough and transparent process of consensus statements. Twenty-four poisons were chosen according to their frequency, available evidence, and relevance. A systematic literature search was performed in order to retrieve all original publications regardless of language. Data were extracted on a standardized instrument. Quality of the evidence was assessed by GRADE as: High = A, Moderate = B, Low = C, Very Low = D. For every poison, dialyzability was assessed and clinical effect of ECTR summarized. All pertinent documents were submitted to the workgroup with a list of statements for vote (general statement, indications, timing, ECTR choice). A modified Delphi method with two voting rounds was used, between which deliberation was required. Each statement was voted on a Likert scale (1-9) to establish the strength of recommendation. This approach will permit the production of the first important practice guidelines on this topic.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it