Bibliographic record
Abstract
Selective Serotonin Reuptake Inhibitors (SSRIs) are common medications taken by patients that are diagnosed with depression or anxiety, and examples of these medications are Fluoxetine, Sertraline, Paroxetine, Fluvoxamine, Citalopram, Escitalopram, and Vilazodone.However, SSRIs have been discovered to cause negative or adverse effects on patients, especially adolescents.This has caused the United States Food and Drug Administration (FDA) to become stricter with the administration of SSRIs, including the requirement for proper labels warning the patient of the adverse effects.This paper investigates adverse events that are associated with SSRI therapy.For example, the SSRI Fluoxetine has been shown to cause adverse effects such as insomnia, anxiety, anorexia, and seizures.Another SSRI is Sertraline, which causes excessive bleeding, which can further lead to platelet aggregation.Also, the SSRI Paroxetine can cause the patient to have drowsiness, sleep disturbance, appetite and Discontinuation Syndrome.Discontinuation Syndrome occurs when an antidepressant is not taken by the individual any more.To add on, the SSRI Citalopram can cause many adverse effects, including diaphoresis, nausea, and vomiting.The SSRI Escitalopram can cause hyponatremia, insomnia, and nausea.Finally, the SSRI Vilazodone can cause patients to experience arthralgia, palpitations, and fatigue.In addition to these adverse effects, there are also Serious Adverse Effects (SAEs).SAEs are adverse events that cause hospitalisation, permanent damage, and death to the patient.An example of an SAE is suicide, which is more likely to happen in adolescents.A study was conducted where 4582 patients were placed in 24 placebo-controlled trails that displayed that antidepressants caused increase in suicide in pediatric patients.Also, the FDA has administered black box warnings on SSRI packaging to notify patients 24 years of age and under that they are at risk of suicide.Many SSRIs cause serious damage, especially in teens, and may be ineffective.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.000 |
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".