Off-label use of medicine in pediatrics
Bibliographic record
Abstract
PURPOSE OF REVIEW: To provide current information on off-label medication use in pediatric gastroenterology, including a discussion on US legislative efforts to address the issue. RECENT FINDINGS: Medications used to treat pediatric gastrointestinal illnesses are frequently prescribed off-label. Acid suppressors, antiemetics, laxatives, and antitumor necrosis factor therapies are types of medications frequently used off-label in the pediatric gastroenterology arena. Pediatric studies conducted under US Federal laws are generating much-needed data on the safety and effectiveness of medications used to treat pediatric patients. Moreover, a new US law, the Food and Drug Administration Safety and Innovation Act, may further the development of pediatric medications in part by requiring pediatric-specific study plans earlier in the overall drug development process. As of today, there still are gaps in our knowledge about these medications, including for the treatment of pediatric gastroenterology diseases. SUMMARY: Medications are widely used off-label in pediatrics, including medications intended to treat gastrointestinal diseases, such as antitumor necrosis factor and laxatives. Although legislation is helping to generate and make available important information about pediatric medications, most still do not contain pediatric data. Therefore, providers need to understand the potential risks and benefits of prescribing off-label products to pediatric patients.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".