Bibliographic record
Abstract
BACKGROUND: Interferons are employed in the management of multiple sclerosis, hepatitis C and certain malignancies. Neuropsychiatric toxicity can interfere with the successful use of these drugs. METHODS: This review was based on Medline literature searches, supplemented by bibliographical citations in identified papers. Information uncovered in the literature review was interpreted in light of related pharmacoepidemiological and psychiatric literature. RESULTS: Interferon-associated neurotoxicity does not adhere closely to standard psychiatric syndromal and diagnostic definitions. Delirium, depression, non-specific symptoms related to sickness behavior and, rarely, manic and psychotic syndromes are all potential adverse events during interferon treatment. For depression, the evidence of increased risk is stronger for interferon alpha than for interferon beta. The availability of preventive and treatment interventions suggest that neuropsychiatric toxicity can often be managed without needing to discontinue the treatment. CONCLUSIONS: Safety can be maximized by organization of health services in ways that enhance detection and management of neuropsychiatric problems, and which support access to basic and specialized mental health services.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".