ReviewRSC advances2026
Machine learning prediction and calibration of cellulose-based solid-phase extraction performance for pharmaceuticals across aqueous matrices.
Review in RSC advances, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cellulose-based solid-phase extraction has been increasingly proposed for concentrating trace pharmaceuticals from complex waters; however, cross-laboratory transfer remains uncertain because studies vary in matrix chemistry, sorbent functionalization, extraction format, elution strategy, and quality control. Evidence from 2015 to 2025 was gathered, and 637 experiments from 36 reports and 28 DOIs were modelled using 29 descriptors of method and matrix. ElasticNet (EN), XGBoost (XGB), and random forest regressor (RFR) were evaluated using study group nested cross-validation with conformal prediction to estimate out-of-study performance and 90% confidence intervals for recovery, matrix recovery ratio (MRR), enrichment factor (EF), limit of detection (LOD), and limit of quantification (LOQ). ElasticNet dominated the sensitivity endpoints, achieving a mean
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.