Plain language summary of publication: expert consensus on the early use of long-acting injectable antipsychotics in people with bipolar I disorder
Bibliographic record
Abstract
Plain Language SummaryWhat is this summary about?This summary highlights key insights from expert psychiatrists on the early use of long-acting injectable (LAI) antipsychotics for treating people with bipolar I disorder.What were the results?LAI antipsychotics are usually reserved as a ‘last resort’ treatment in people with bipolar I disorder, even though evidence supports their early use. Barriers to earlier use of LAIs include access issues and views that healthcare providers and people with bipolar I disorder have about this type of medication. If the treatment plan of people with bipolar I disorder includes antipsychotics, experts recommend using LAIs as early as possible to help improve long-term outcomes, as part of a shared decision-making approach that involves healthcare providers, people with bipolar I disorder, and their caregivers.What do the results mean?The recommendations offer ways to support informed treatment decisions and improve the care of people with bipolar I disorder.How to say (download PDF and double click sound icon to play sound)…Antipsychotic: an-tie-sy-COT-ickAripiprazole: a-ri-PIP-ruh-zolBipolar disorder: by-POL-luh dis-or-derMania: MAY-nee-uhPsychosis: sy-KOH-sisLong-acting injectable (LAI): A form of medication that is given via a needle into the body and is released in the body over a long period of time.Antipsychotics: Medicines that help manage symptoms of mental health conditions like bipolar disorder.Bipolar I disorder: A long-term mental health condition where individuals have intense mood swings, which can range from depression to mania.This is an abstract of the Plain Language Summary of Publication article.View the full Plain Language Summary PDF of this article to read the full-textLink to original article here
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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 | 0.057 | 0.325 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.075 | 0.036 |
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".