Evaluation of a novel instrument for detecting bipolar disorders in China: The Rapid Mood Screener (RMS)
Bibliographic record
Abstract
OBJECTIVE: Bipolar disorder is easily misdiagnosed with major depressive disorder (MDD). The Rapid Mood Screener (RMS) was developed to address this unmet clinical need. This study aims to translate and evaluated the reliability and validity of the RMS in Chinese adults with bipolar I/II disorder (BD-I/II). METHODS: Brislin's translation and Delphi method were conducted to formulate the RMS-Chinses version (RMS-C). Patients with MDD (N = 99), BD-I (N = 77) and BD-II (N = 78) were included to assess the validity and reliability of RMS-C. The area under the curve (AUC) was computed to ascertain the ability of the Mood Disorder Questionnaire (MDQ) and RMS-C to distinguish BD-I and BD-II from MDD. The optimal cut-off scores for classification were also calculated by the maximum sensitivity and specificity. RESULTS: The intraclass correlation coefficient of the RMS-C was 0.82 (95%CI, 0.71-0.89). The content validity index by six items were 0.71, 0.86, 1.00, 0.86, 1.00, and 1.00 in turn, and by scales was 0.90. The AUCs of the RMS-C in both BD-I/II, BD-I alone and BD-II alone were 0.83 (95 % CI, 0.78-0.89), 0.82 (95 % CI, 0.75-0.89) and 0.85 (95 % CI, 0.79-0.91), respectively, and were comparably to the MDQ. The optimal RMS-C values of the presence of BD-I and BD-II were >4 and 3, respectively. CONCLUSION: The RMS-C is a valid, simple self-administer screening tool to help identify BD-I or BD-II in persons experiencing a depressive episode. Validating the impact of screening with the RMS-C on health outcomes and health economics is warranted.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".