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Record W2081580861 · doi:10.1111/sdi.12245

Case Reports of Extracorporeal Treatments in Poisoning: Historical Trends

2014· article· en· W2081580861 on OpenAlexaff
Joëlle Mardini, Valéry Lavergne, Darren M. Roberts, Marc Ghannoum

Bibliographic record

VenueSeminars in Dialysis · 2014
Typearticle
Languageen
FieldMedicine
TopicPoisoning and overdose treatments
Canadian institutionsHôpital du Sacré-Cœur de MontréalUniversité de Montréal
Fundersnot available
KeywordsHemoperfusionMedicineExtracorporealMethanol poisoningHemodialysisIntensive care medicineSurgery

Abstract

fetched live from OpenAlex

There are currently limited data on the trends in case reporting of poisoned patients undergoing enhanced elimination with an extracorporeal treatment (ECTR). The present manuscript specifically reviews the longitudinal trends of reports according to technique, poison, and country of publication. To identify case reports of ECTR use in the management of poisoning, multiple databases were searched. There were no limitations on language and year of publication. All case reports describing individual patients undergoing ECTR with the intent of enhancing the elimination of a poison were included in the analysis. Since 1913, 2908 reports were identified. There were an increasing number of published reports with time except for a slight decrease during the 1990s. Hemodialysis was by far the most commonly used ECTR in poisoning, followed by hemoperfusion. The number of reported peritoneal dialyses decreased steadily since 1980s. Methanol, ethylene glycol, lithium, and salicylates remained among the most commonly reported poisons in every decade. The large majority of publications originated from either Europe or North America, and more specifically from the United States, Germany, the United Kingdom, and China. Despite the emerging apparition of new techniques, hemodialysis remains to this day the favoured ECTR in the treatment of poisoned patients.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.093
Threshold uncertainty score0.700

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.017
GPT teacher head0.279
Teacher spread0.262 · 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 teacher head, not a consensus.

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

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

Citations22
Published2014
Admission routes1
Has abstractyes

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