Clinical predictors of non‐response to lithium treatment in the Pharmacogenomics of Bipolar Disorder (PGBD) study
Bibliographic record
Abstract
BACKGROUND: Lithium is regarded as a first-line treatment for bipolar disorder (BD), but partial response and non-response commonly occurs. There exists a need to identify lithium non-responders prior to initiating treatment. The Pharmacogenomics of Bipolar Disorder (PGBD) Study was designed to identify predictors of lithium response. METHODS: The PGBD Study was an eleven site prospective trial of lithium treatment in bipolar I disorder. Subjects were stabilized on lithium monotherapy over 4 months and gradually discontinued from all other psychotropic medications. After ensuring a sustained clinical remission (defined by a score of ≤3 on the CGI for 4 weeks) had been achieved, subjects were followed for up to 2 years to monitor clinical response. Cox proportional hazard models were used to examine the relationship between clinical measures and time until failure to remit or relapse. RESULTS: A total of 345 individuals were enrolled into the study and included in the analysis. Of these, 101 subjects failed to remit or relapsed, 88 achieved remission and continued to study completion, and 156 were terminated from the study for other reasons. Significant clinical predictors of treatment failure (p < 0.05) included baseline anxiety symptoms, functional impairments, negative life events and lifetime clinical features such as a history of migraine, suicidal ideation/attempts, and mixed episodes, as well as a chronic course of illness. CONCLUSIONS: In this PGBD Study of lithium response, several clinical features were found to be associated with failure to respond to lithium. Future validation is needed to confirm these clinical predictors of treatment failure and their use clinically to distinguish who will do well on lithium before starting pharmacotherapy.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".