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Record W4405838580 · doi:10.1097/cxa.0000000000000223

Trending Urine Cocaine/BE Levels for Patient Advocacy: Case Report

2024· article· en· W4405838580 on OpenAlexaffvenue
Jillian Macklin, Cristiana Stefan, Tianna Costa, Nitin Chopra

Bibliographic record

VenueThe Canadian Journal of Addiction · 2024
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicForensic Toxicology and Drug Analysis
Canadian institutionsCentre for Addiction and Mental HealthUniversity of Toronto
Fundersnot available
KeywordsMedicineAddictionAbstinencePresentation (obstetrics)UrineIllicit drugDrugImmunoassayPsychiatryFamily medicineInternal medicineSurgeryImmunology

Abstract

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ABSTRACT Objective: The primary objective of this case report is to highlight and discuss, for the first time to our knowledge, the role of semi-quantitative immunoassay urine drug screen (UDS) trends using levels normalized to creatinine in addiction psychiatry, especially when not correlating with clinical presentation. The secondary objective is to capture the benefits of collaborative care with laboratory medicine in addiction medicine. Methods: We describe the case of a patient’s longitudinal journey through 2 inpatient admissions and outpatient encounters using semi-quantitative immunoassay UDS data to correlate results with clinical presentation and self-report of cocaine use. We then completed an in-depth review of the literature to gain a better understanding of immunoassays, cocaine detection windows, and excretion patterns. Results: We highlight that trending semi-quantitative UDS levels normalized to creatinine are helpful in answering questions related to new drug use in the hospital or residual drug elimination following abstinence that may guide discharge planning from the hospital. We also highlight our medical system has biases toward substance use, and we must focus on treating the patient, not the levels. Conclusion: This case report shows the role of semi-quantitative immunoassay urine drug screen trends in addition psychiatry, and shows the strength of collaborative care with laboratory medicine to advocate for future patients. Objectif: L’objectif principal de ce rapport de cas est de mettre en évidence et de discuter, pour la première fois à notre connaissance, le rôle des tendances des tests immunologiques semi-quantitatifs de dépistage des drogues dans l’urine (DDU) en utilisant des niveaux normalisés à la créatinine en psychiatrie de l’addiction, en particulier lorsqu’il n’y a pas de corrélation avec la présentation clinique. L’objectif secondaire est d'évaluer les avantages d’une collaboration avec la médecine de laboratoire dans le domaine de l’addiction. Méthodes: Nous décrivons le parcours longitudinal d’un patient au cours de deux hospitalisations et de consultations externes en utilisant les données du DDU par immunodosage semi-quantitatif pour corréler les résultats avec la présentation clinique et l’auto-déclaration de l’usage de cocaïne. Nous avons ensuite procédé à un examen approfondi de la littérature afin de mieux comprendre les immunodosages, la durée de détection de la cocaïne et les schémas d’excrétion. Résultats: Nous soulignons que la tendance des niveaux semi-quantitatifs du DDU normalisés à la créatinine est utile pour répondre aux questions liées à la nouvelle consommation de drogue à l’hôpital ou à l'élimination de la drogue résiduelle après l’abstinence, ce qui peut guider la planification de la sortie de l’hôpital. Nous soulignons également que notre système médical a des préjugés à l'égard de la consommation de substances et que nous devons nous concentrer sur le traitement du patient, et non sur les taux. Conclusion: Ce rapport de cas montre le rôle des tendances des tests immunologiques semi-quantitatifs de dépistage de drogues dans l’urine dans la psychiatrie de l’addiction, et montre la force des soins collaboratifs avec la médecine de laboratoire pour défendre les intérêts des futurs patients.

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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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.273
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.091
GPT teacher head0.394
Teacher spread0.304 · 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.

Study designNot applicable
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

Citations1
Published2024
Admission routes2
Has abstractyes

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