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Record W7161600299 · doi:10.55197/qjmhs.v1i2.17

ACUTE ORGANOPHOSPHORATE POISONING: CLINICAL, DIAGNOSTIC AND THERAPEUTIC APPROACH

2021· article· W7161600299 on OpenAlexaff
RAFAEL MEDINA LUCERO, Juan Farak Gómez, MAURICIO DIAZ CARRASCAL, JUAN CASTILLA ORTEGA, OSCAR SANCHEZ ANDRADE, NELLYS QUINTERO RINCONES

Bibliographic record

VenueQuantum Journal of Medical and Health Sciences · 2021
Typearticle
Language
FieldAgricultural and Biological Sciences
TopicPesticide Exposure and Toxicity
Canadian institutionsMisericordia Community Hospital
Fundersnot available
KeywordsMuscarinic acetylcholine receptorOrganophosphate poisoningNicotinic agonistOrganophosphateAcetylcholinesteraseAcetylcholineCholinergic

Abstract

fetched live from OpenAlex

Organophosphate poisoning is considered pesticides that have the ability to inhibit the enzyme Acetylcholinesterase to produce an overstimulation of muscarinic and nicotinic receptors present in the CNS, exocrine glands, hollow organs, among others. A narrative review was carried out based on keywords of poisoning, organophosphate, poison and acetylcholine in between 2015 and 2020. Organophosphate poisoning mainly presents in cholinergic syndrome product of overstimulation of the muscarinic receptors. Subacutely, it can present as intermediate syndrome and subsequent consists polyneuropathy induced by organophosphates. The clinical findings pesticide exposure will provide the diagnosis. In the driving initial symptomatic should use atropine, which will act in a competitive with ACh for muscarinic and nicotinic receptors, but the reversal of the action of the organophosphate on AChE is only carried out with the oximes, pradiloxime is the first choice. The reviewed provides updated information about key aspects in organophosphate poisoning as well as a approach in the clinical presentation.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.112
GPT teacher head0.384
Teacher spread0.273 · 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 designNot applicable
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

Citations0
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueQuantum Journal of Medical and Health SciencesSame topicPesticide Exposure and ToxicityFrench-language works237,207