Evidence-based strategies improve assessment of pediatric bipolar disorder by community practitioners.
Bibliographic record
Abstract
The misdiagnosis of pediatric bipolar disorder (PBD) has become a major public health concern. Would available evidence-based assessment (EBA) strategies help improve diagnostic accuracy, and are clinicians willing to consider these strategies in practice? The purpose of the present study was to document the extent to which using an EBA decision tool--a probability nomogram--improves the interpretation of family history and test data by clinicians and to examine the acceptability of the nomogram technique to clinicians. Over 600 clinicians across the US and Canada attending continuing education seminars were trained to use the nomogram. Participants estimated the probability that a youth in a clinical vignette had bipolar disorder, first using clinical judgment and then using the nomogram. Brief training of clinicians (less than 30 minutes) in using the nomogram for assessing PBD improved diagnostic accuracy, consistency, and agreement. The majority of clinicians endorsed using the nomogram in practice. EBA decision aids, such as the nomogram, may lead to a significant decrease in overdiagnosis and help clinicians detect true cases of PBD.
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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 | 0.074 | 0.393 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".