The dangers of substance abuse in adolescents with chronic kidney disease: a review of the literature
Bibliographic record
Abstract
Although there exist no specific data on the prevalence of substance abuse among children and adolescents with chronic kidney diseases (CKD), the magnitude of this problem should not be underestimated, as almost half of twelfth-graders in the U.S. admit to a history of using illegal drugs at least once when asked (National Institute on Drug Abuse, 2011). According to the 2010 Canadian Alcohol and Drug Use Monitoring Survey (Health Canada, n.d.), the prevalence of drug abuse among Canadian youths and young adults aged 15 to 24 remains higher than in adults older than 25 years of age, and the rates of drug use (excluding cannabis) in the past years were 7.9% and 0.8%, respectively, illustrating an almost 10 times higher rate in the younger age group (Health Canada, n.d.). Drug abuse can lead to numerous medical problems, including renal injury, and it is clearly a major public health concern, especially in patients with subnormal kidney function (Vupputuri et al., 2004). As most of the children and adolescents that suffer from CKD have long-term and trustful relationships with the nephrology team, we have the obligation and are in an excellent position to address this particular health issue (Finkelstein & Finkelstein, 2000; Kimmel, 2002; Kimmel, Cohen, & Peterson, 2008). This review summarizes the available data on the nephrotoxic effects of various commonly abused drugs with special emphasis on the additional damage that occurs in patients with pre-existing CKD. These data were obtained from a thorough search of the available primary literature, specifically using the PubMed database. The purpose is to provide health professionals with a resource to properly educate their CKD patients on the dangers of these drugs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".