Insulin: a commonly used high‐risk medicine
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
Abstract Type 2 diabetes is common and is associated with progressive beta cell loss, insulin deficiency, organ damage and effects on mental health and wellbeing. The current management focus is on stringent blood glucose control (HbA 1c <7% [53mmol/mol]) and early insulin initiation. Insulin is a high‐risk medicine and is associated with a high rate of errors, adverse events and admissions to hospital. An insulin high‐risk medicine alert and accompanying audit tool were developed and distributed to Australian hospitals in Victoria in 2009. The purpose of this paper is to outline the self‐reported impact of the insulin alert on hospital insulin management policies, discuss the lessons learned from the process, and suggest strategies that could be more effective when other medicine alerts are disseminated. The insulin alert, audit tool and an anonymous self‐complete questionnaire were mailed to the chief executive officers of 90 hospitals who distributed them to their relevant quality and safety governance committees for action. Only 26 hospitals responded (29%). Respondents reported that the insulin alert triggered them to review insulin policies and procedures, develop insulin education programmes and review hypoglycaemia management. They did not provide information about the impact on insulin errors. Respondents found the audit tool time consuming because the form was very long and not available in electronic form. Diabetes clinicians did not appear to have been involved. The key lessons learned were that relying on a passive implementation process, self‐report, and long, written audit tools are unlikely to engender change. Processes need to be tailored to suit individual organisations and engage key local clinical leaders. Outcomes/impact need to be measured objectively. Copyright © 2012 John Wiley & Sons.
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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 | 0.003 | 0.081 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it