Reducing neuroimaging in first-episode psychosis by facilitating uptake of choosing wisely recommendations: a quality improvement initiative
Bibliographic record
Abstract
First-episode psychosis (FEP) is a frequent presentation to hospital.1 Neuroimaging is often ordered during the initial assessment to facilitate diagnostic buy-in, mitigate medico-legal concerns, and maximise assessment in a patient population that is typically marginalised.2 3 These benefits, however, are outweighed by the unnecessary radiation, cost, and strong evidence that neuroimaging rarely yields actionable findings.2 3 This stance is evidenced in international guidelines2 3 and Choosing Wisely Canada (CWC) recommendations.4 Our site’s radiology department queried inconsistent (per guidelines) CT imaging in FEP, given requisition patterns. We undertook a quality improvement initiative to determine the prevalence of inconsistent imaging and study the effectiveness of an intervention aimed at improving recommendation adherence. ### Data collection All CT head studies with FEP-related indications performed at two local hospitals were identified through keyword searches of radiology reports containing any of the following terms: bizarre, delusion(s), delusional, hallucination(s), psychosis and psychotic. Extracted data included ordering physician specialty, provided indication, and imaging findings. Studies were classified as consistent or inconsistent with CWC guidelines.4 ‘Later-age of onset’, a CWC criterion for neuroimaging, is not defined by CWC and was chosen a priori to be age 40 years or more, based on local expert consensus and literature suggestions ranging from 35 to 50.2 3 5–8 Chart review was used to clarify history and imaging indications when ambiguous requisition information was provided. All ambiguous cases were settled through consensus (RS and RDH). Pre-intervention data collection was performed retrospectively from January to June 2018 (6 months). Although a 6-month post-audit period would have been sufficient based off a predetermined sample size calculation, we expanded the post-intervention period to 12 months to assess the durability of findings. ### Intervention Following collection of baseline pre-audit data, multidisciplinary stakeholders were consulted to develop study priorities, design, and interventions. Engaged stakeholders included …
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How this classification was reachedexpand
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 | 0.036 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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; both teacher heads 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".