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 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 | 0.336 | 0.582 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.009 | 0.014 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".