P282 Management Of Patients With Bipolar Disorder: An Adapted Clinical Practice Guideline >From King Saud University, King Khalid University Hospital, Clinical Practice Guidelines Committee, Faculty Of Medicine, Department Of Psychiatry
Bibliographic record
Abstract
Objectives Adaptation of CPGs for Treatment of Bipolar disorder in King Khalid University Hospital, Psychiatry Department Methods The ADAPTE process for CPGs adaptation. Results: the final decision of the panel after full assessments of 3 source CPGs was full acceptance (adoption) of the Canadian Network for Mood and Anxiety Treatments (CANMAT) and International Society for Bipolar Disorders (ISBD) collaborative update of CANMAT guidelines for the management of patients with bipolar disorder (updated 2009). Results Examples of Recommendations Lithium, valproate, and several atypical antipsychotics monotherapy is recommended to be used as first line treatments for acute mania, combination pharmacology with antipsychotics and mood stabiliser can be used as first line option. Paliperidone monotherapy and asenapine alone or in combination with lithium or divalproex can be used as a second line treatment; tamoxifen is suggested as a third line augmentation option. For the Management of bipolar depression, lithium, lamotrigine, and quetiapine monotherapy, olanzapine plus selective serotonin reuptake inhibitor (SSRI), and lithium or divalproex plus SSRI bupropion are first-line options. Adjunctive modafinil is recommended as a second-line option. lithium, lamotrigine, valoproate and olanzapine are first-line options for maintenance treatment of bipolar disorder. Conclusion New data support the use of quetiapine monotherapy and adjunctive therapy for the prevention of manic and depressive events, aripiprazole monotherapy for the prevention of manic events, and risperidone long-acting injection monotherapy and adjunctive therapy, and adjunctive ziprasidone for the prevention of mood events.
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 | 0.022 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".