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Record W2476707892 · doi:10.1111/sdi.12520

Why are we Still Dialyzing Overdoses to Tricyclic Antidepressants? A subanalysis of the <scp>NPDS</scp> database

2016· review· en· W2476707892 on OpenAlexaff
Valéry Lavergne, Robert S. Hoffman, James B. Mowry, Monique Cormier, Sophie Gosselin, Darren M. Roberts, Marc Ghannoum

Bibliographic record

VenueSeminars in Dialysis · 2016
Typereview
Languageen
FieldMedicine
TopicPoisoning and overdose treatments
Canadian institutionsAlberta Health ServicesMcGill University Health CentreUniversité de Montréal
Fundersnot available
KeywordsTricyclicMedicinePharmacology

Abstract

fetched live from OpenAlex

A recent analysis of the American Association of Poison Control Centers database, showed that poisonings from toxins not usually considered amenable to extracorporeal purification ("non-classic toxins" such as ethanol and tricyclic antidepressants) continue to be reported. This publication investigates factors that may explain these findings. Our results suggest that: 1) the relatively high absolute number of ECTR performed for non-classic toxins may simply reflect the large number of exposures to these toxins, 2) poisoning from another toxin may have been the reason for ECTR initiation in some exposures to non-classic toxins, 3) poisoning from non-classic toxins may receive ECTR for purposes other than toxin removal, and 4) the decisional threshold to initiate ECTR may be lower for non-classic toxins because of heightened toxicity.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.005
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.042
GPT teacher head0.340
Teacher spread0.297 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreReview

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".

Quick stats

Citations6
Published2016
Admission routes1
Has abstractyes

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