Description and Consequences of Prescribing Off-Label Antiretrovirals in the Madrid Cohort of HIV-Infected Children over a Quarter of a Century (1988–2012)
Bibliographic record
Abstract
BACKGROUND: Licensing data for paediatric dosing is often sparse and subsequent studies may result in changes to recommended doses. We measured the extent and consequences of off-label antiretroviral (ARV) use in an HIV-infected paediatric cohort. METHODS: In this multicentre cohort study involving 318 HIV-infected children and adolescents from the Madrid Cohort, all off-label prescriptions from March 1988 to March 2012 were recorded from the clinical records. The reasons for prescribing ARV off-label, the side effects and the consequences of incorrect dosing of ARVs are discussed. RESULTS: Among the 318 patients of the cohort, 221 (69%) received off-label ARVs according to EMA licensing at the time of prescription, representing 23% (540) of the 2,353 prescribed ARVs. The main reason for starting an off-label drug was treatment failure. Adverse events led to treatment discontinuation in 12% of the prescriptions. Problems taking the drug led to withdrawal in 5%, more likely when formulation was not suitable for age (P<0.05). Up to 10% were overdosed and 10% underdosed, defined as 25% above or below the current recommended dose, respectively. Treatment failure occurred significantly more frequently among underdosed compared to overdosed patients (50% versus 26%; P<0.05). CONCLUSIONS: Off-label use of ARVs was common in our HIV-1 paediatric patients. Adverse events were common but rarely led to withdrawal. Suitable formulation is important in younger children. Pharmacokinetic studies are needed as frequent incorrect dosing may occur when prescribing off-label and underdosing may lead to treatment failure.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".