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Record W4313474640 · doi:10.22517/25395203.24982

Vasculitis por cocaína-levamisol, manifestaciones cutáneas, articulares y gastrointestinales: reporte de caso

2022· article· es· W4313474640 on OpenAlexaff
José Julián Aristizábal Hernández, María Paulina Villa Saldarriaga, María Carolina Rave-Aguirre, Juliana Ceballos-Giraldo, Ana Mercedes Vanegas-Torres, Miguel Antonio Mesa Navas

Bibliographic record

VenueRevista Médica de Risaralda · 2022
Typearticle
Languagees
FieldPharmacology, Toxicology and Pharmaceutics
TopicForensic Toxicology and Drug Analysis
Canadian institutionsBell (Canada)
Fundersnot available
KeywordsMedicineVasculitisHumanitiesInternal medicinePhilosophy

Abstract

fetched live from OpenAlex

El levamisol es un antiparasitario de uso veterinario que actualmente está siendo empleado para aumentar el volumen y la potencia de la cocaína y así obtener más ganancias en su comercialización. La mezcla de estas dos sustancias puede causar el síndrome por cocaína-levamisol, caracterizado por lesiones propias de la cocaína como la afección del cartílago septal y perforación del tabique nasal, vasculitis cutánea de pequeños vasos, así como un compromiso vasculítico específico de los pabellones auriculares y del cartílago nasal, que puede avanzar a necrosis e incluso ulceración asociada a agranulocitosis, artralgias y, algunas veces, glomerulonefritis . Se describe un paciente con vasculitis asociada a síndrome por cocaína-levamisol con manifestaciones cutáneas y articulares, además de gastrointestinales. El paciente presentó una respuesta adecuada al manejo con esteroides, pero ante la recurrencia requirió manejo adicional con ciclofosfamida para control total de los síntomas

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: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.043
GPT teacher head0.369
Teacher spread0.326 · 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 designCase report
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

Citations0
Published2022
Admission routes1
Has abstractyes

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