Evidence-based strategies improve assessment of pediatric bipolar disorder by community practitioners.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it