Poster Session I
Bibliographic record
Abstract
Introduction: Depressive episodes/symptoms of bipolar I disorder (BPD-I) are commonly misdiagnosed as major depressive disorder (MDD).We developed a brief, self-rated, pragmatic tool that screens for manic symptoms and identifies BPD-I risk factors (eg, age of onset) to reduce misdiagnosis of BPD-I as MDD.Method: Existing questionnaires and risk factors were identified through a targeted literature search to select concepts thought to differentiate BPD-I from MDD.Individuals with self-reported BPD-I or MDD (N = 12) participated in cognitive debriefing interviews to test and refine item wording.An observational study was conducted to evaluate the tool's predictive validity.Participants with clinical interview-confirmed diagnoses of BPD-I or MDD completed a 10item screening tool and other questionnaires.Data were analyzed to identify a smaller subset of items with optimized sensitivity and specificity.Results: Of 160 interviews conducted, 139 patients had confirmed BPD-I (n = 67) or MDD (n = 72).The screening tool was reduced from 10 to 6 items based on item-level analysis.When 4 items or more were endorsed ("yes"), the performance of this tool for identifying patients with BPD-I was 0.92 and specificity was 0.78; positive and negative predictive values, based on the analysis sample, were 0.78 and 0.92, respectively.These properties represent an improvement over the Mood Disorder Questionnaire, while using >50% fewer items.Conclusion: This brief and valid screening tool serves to identify patients with depressive symptoms who may have BPD-I instead of MDD, prompting more comprehensive clinical assessment, improved diagnostic accuracy and treatment selection, and enhanced health outcomes in busy clinical practices.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.788 | 0.576 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".