The Rapid Mood Screener (RMS): a novel and pragmatic screener for bipolar I disorder
Bibliographic record
Abstract
Objective Depressive episodes and symptoms of bipolar I disorder are commonly misdiagnosed as major depressive disorder (MDD) in primary care. The novel and pragmatic Rapid Mood Screener (RMS) was developed to screen for manic symptoms and bipolar I disorder features (e.g. age of depression onset) to address this unmet clinical need.Methods A targeted literature search was conducted to select concepts thought to differentiate bipolar I from MDD and screener tool items were drafted. Items were tested and refined in cognitive debriefing interviews with individuals with self-reported bipolar I or MDD (n = 12). An observational study was conducted to evaluate predictive validity. Participants with clinical interview-confirmed bipolar I or MDD diagnoses (n = 139) completed a draft 10-item screening tool and other questionnaires. Data were analyzed to identify the smallest possible subset of items with optimized sensitivity and specificity.Results Adults with confirmed bipolar I (n = 67) or MDD (n = 72) participated in the observational study. Ten draft screening tool items were reduced to 6 final RMS items based on the item-level analysis. When 4 or more items of the RMS were endorsed (“yes”), sensitivity was 0.88 and specificity was 0.80; positive and negative predictive values were 0.80 and 0.88, respectively. These properties were an improvement over the Mood Disorder Questionnaire in the same analysis sample while using 60% fewer items.Conclusion The pragmatic 6-item RMS differentiates bipolar I disorder from MDD in patients with depressive symptoms, providing real-world guidance to primary care practitioners on whether a more comprehensive assessment for bipolar I disorder is warranted.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".