Prescribing lithium for the management of persons suffering from bipolar disorders: expert consensus based on a Delphi study
Bibliographic record
Abstract
BACKGROUND: According to international guidelines, lithium treatment represents the gold standard for the appropriate management of persons with bipolar disorder. However, prescription rates in ordinary practice are not in line with clinical guidelines' suggestions. Clinicians prefer to use drugs other than lithium, considering its low therapeutic window, the need for regular lab tests and its side effects profile. Based on these premises, a Delphi-method study focused on highly-debated aspects of lithium treatment in bipolar disorder has been promoted with the aim to reach a consensus among an expert panel of Italian psychiatrists. METHODS: The Delphi method is a structured technique aimed to obtain a consensus from repeated rounds of questionnaires where opinion/agreement among experts are important. A Steering Committee of experts has developed a 24-items questionnaire exploring: (1) the use of lithium as first choice for treating different phases of bipolar disorder; (2) the side effect and tolerability profile of lithium treatment as hampering factors for its use in clinical practice; (3) the lithium prescribing in special target population, such as adolescents, elderly patients, and pregnant women. RESULTS: The questionnaire was delivered to a panel of 100 Italian psychiatrists, experts in the field of managing people with bipolar disorders. An almost complete positive consensus was reached for statements dealing with the use of lithium treatment as first choice in the management of patients with bipolar disorder, and as the first choice for preventing manic/hypomanic and depressive episodes. CONCLUSIONS: Current clinical guidelines and scientific evidence support the use of lithium as first choice treatment in patients with bipolar disorder. However, over the last decades a downward tendency in lithium's prescription has been registered worldwide. The present Delphi study confirmed the "good clinical reasons" for supporting lithium prescription in clinical practice. Our findings should be used to develop clinical practice guidelines and reduce the discrepancy between international guidelines and ordinary care.
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 | 0.095 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".