The Tolerability and Side Effect Profile of Venlafaxine in a Clinical Outpatient Pediatric Population
Bibliographic record
Abstract
Background: Major depressive disorder (MDD) in youth is a prevalent and debilitating mental health condition, with approximately 20% of adolescents estimated to be affected prior to adulthood.Objective: Our objective was to review the tolerability and efficacy of venlafaxine in adolescents with MDD.Methods: We conducted a retrospective chart review spanning January 2020 to December 2022 from the Royal Ottawa Mental Health Centre.Patients were included if they were between 7 and 18 years old, had a diagnosed MDD, and were on Venlafaxine for at least six weeks and were excluded if they didn't have enough follow-ups, resulting in a sample size of 45 patients.The primary outcomes were the tolerability and side effect profile of venlafaxine, assessed through changes in symptomatology, documented new symptoms, and functional side effects.Results: The study population comprised 67% females (n=30).Males were treated significantly longer (19 months) than females (9.61 months) (p=.015).Most patients (53.3%) maintained a consistent dosage throughout treatment.Approximately 11.1% of patients experienced new symptoms, and 6.7% reported functional side effects.Improvement in mood was noted in 51.1% of patients, with additional benefits observed in sleep (26.7%), anxiety reduction (24.4%), and mood stabilization (15.6%).Overall, 68.9% of patients reported improvements in symptoms over the course of treatment.Conclusions: Venlafaxine appears to be a welltolerated and effective treatment option for adolescents with MDD.These findings support the potential use of venlafaxine in cases where first-line treatments are inadequate, highlighting the need for further research on its safety and efficacy.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".