MétaCan
Menu
Back to cohort
Record W2566309191 · doi:10.1097/md.0000000000005461

Treatment of a long-acting anticoagulant rodenticide poisoning cohort with vitamin K1 during the maintenance period

2016· article· en· W2566309191 on OpenAlexaff
Jianhai Long, Xiaobo Peng, Yuan Luo, Yawei Sun, Guo-Dong Lin, Yongan Wang, Zewu Qiu

Bibliographic record

VenueMedicine · 2016
Typearticle
Languageen
FieldMedicine
TopicPoisoning and overdose treatments
Canadian institutionsEmergent BioSolutions (Canada)
Fundersnot available
KeywordsRodenticideMedicineBrodifacoumVitaminAnticoagulantWarfarinToxicantAnesthesiaToxicologyInternal medicineToxicityAtrial fibrillation

Abstract

fetched live from OpenAlex

Currently, there are few guidelines for the use of vitamin K1 in the maintenance treatment of long-acting anticoagulant rodenticide (LAAR) poisonings. We explored factors in the treatment of LAAR poisoning during the maintenance period in order to suggest feasible treatment models.Data from 24 cases of anticoagulant rodenticide poisoning in our hospital were collected from January 2013 to May 2016. The patients' sex, age, coagulation function, total time from poisoning to treatment with vitamin K1 (prehospital time), vitamin K1 sustained treatment time (VKSTT), anticoagulant rodenticide category, and specific poison dosage were collected. Multivariate analysis was used to evaluate the correlation between vitamin K1 dosage and other factors during the maintenance period.Only VKSTT (partial regression coefficient -1.133, 0.59, P = 0.035) had an obvious influence on the therapeutic dose of vitamin K1 required during the maintenance period.After an initial pulse therapy, the bleeding and coagulation functions were stabilized, and the patients were subsequently treated with vitamin K1 during the maintenance period. Over time, the maintenance dose of vitamin K1 (10-120 mg/d, intravenous drip) was gradually decreased and was not related to toxicant concentration.

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 score0.010
Threshold uncertainty score0.382

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.013
GPT teacher head0.265
Teacher spread0.252 · 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

Citations10
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueMedicineSame topicPoisoning and overdose treatmentsFrench-language works237,207