Les médicaments qui interfèrent avec les bilans biologiques : revue de la littérature
Bibliographic record
Abstract
Contexte : Le bilan biologique fait partie intégrante du processus de diagnostic qui oriente les décisions de prise en charge thérapeutique. Cependant, ces analyses restent sujettes à des interférences endogènes ou exogènes qui altèrent le résultat. Objectif : L’objectif de notre travail était de fournir un aperçu actualisé et complet des interférences les plus documentées dues aux médicaments, afin que l’interprétation des résultats soit fiable et la prise en charge du patient, meilleure. Sources des données : Il s’agit d’une revue systématique exhaustive de la littérature réalisée en 2018. La recherche bibliographique a été réalisée dans différentes bases de données en ligne, à savoir Pubmed, ScienceDirect et Google Scholar. Sélections des études : Seules les publications en français ou en anglais concernant les médicaments à usage humain ont été retenues. Les interférences avec les examens biologiques, dues aux médicaments, que les investigateurs ont étudiées, concernaient uniquement le dosage sanguin (sérum / plasma). Extraction des données : Un tableur Excel a servi à exploiter les résultats. Au total, 82 articles ont été retenus. Les interférences étudiées touchaient 47 paramètres biologiques correspondant à différents bilans : bilan hormonal, bilan hépatique, bilan rénal. Synthèse des données : Les mécanismes rapportés dans notre littérature étaient à 56,9 % d’ordre analytique, à 17,82 % d’ordre physiologique et à 20,11 % d’ordre pharmacologique. Le reste des mécanismes (5,17 %) n’étaient pas définis. Conclusions : Les cliniciens devraient être vigilants lors de la validation et de l’interprétation des résultats d’un examen biologique pour les patients recevant ces types de médicaments. Enfin le dialogue clinico-biologiste est la meilleure garantie pour éviter des explorations complémentaires inutiles, souvent lourdes et coûteuses. ABSTRACT Background: Biological assessment is an integral part of the diagnostic process that guides therapeutic management decisions. However, these analyses remain subject to interference from endogenous or exogenous factors, which may alter the results. Objective: To provide an up-to-date and comprehensive overview of the most commonly documented types of interference attributable to medications, to ensure reliable interpretation of test results and better management of patients. Data Sources: This comprehensive systematic review of the literature was carried out in 2018. The bibliographic search was carried out in various online databases, specifically PubMed, ScienceDirect and Google Scholar. Study Selection: Only publications in French or English concerning medicinal products for human use were retained. The investigators’ examination of drug-related interference with laboratory tests was limited to blood assays (serum or plasma). Data Extraction: An Excel spreadsheet was used to analyze the results. A total of 82 articles were selected. The interferences studied affected 47 biological parameters corresponding to various types of assessment: hormonal, hepatic, and renal. Data Synthesis: The mechanisms reported in the literature identified were analytical (56.9%), physiological (17.82%), and pharmacological (20.11%). The remainder of the mechanisms (5.17%) were not defined. Conclusions: Clinicians should be vigilant in validating and interpreting laboratory test results for patients receiving these types of drugs. Dialogue between clinicians and biological scientists is the best way to avoid unnecessary additional testing, which is often cumbersome and costly.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".