Actualización en el uso y el manejo del litio en neuropsiquiatría
Bibliographic record
Abstract
Lithium is an alkaline metal, used for more than 60 years in psychiatry, and currently considered the gold standard in the treatment of bipolar disorder (BD). According to recent evidence, this active ingredient is useful for the treatment of a wide spectrum of clinical varieties of affective disorders. In addition, it is estimated that lithium reduces the risk of suicide and suicidal behavior in people with mood disorders. On the other hand, some novel studies have shown that the cation has a potential efficacy for the treatment of other neuropsychiatric processes, such as the likelihood of reducing the risk of dementia and slowing down the development of neurodegenerative diseases. Despite the enormous evidence in favor of the use of lithium, it is known that, in Argentina, medications containing it are prescribed less than expected. In view of all this, the Asociación Argentina de Psiquiatría Biológica (Argentine Association of Biological Psychiatry) (AAPB or AABP) convened a group of experts to review the available scientific literature and prepare an updated document on the management and use of lithium in neuropsychiatry. In addition to the use of the ion in daily clinical practice, the scope of this review includes other contents that have been considered of interest for the psychiatrist, such as certain pharmacological and pharmacogenetic aspects, possible clinical predictors of response to treatment with lithium, management of ion during perinatal period, management of lithium in child and adolescent population, management of adverse effects linked to cation and interactions with drugs and other substances.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".