To Titrate or Not to Titrate: Factors influencing inpatient insulin management by residents and medical students
Bibliographic record
Abstract
The prevalence of diabetes continues to rise, making inpatient management of diabetes an increasingly relevant issue. In teaching hospitals such as those affiliated with the University of Calgary, inpatient management of diabetes is often the responsibility of learners including residents and medical students. Several studies have demonstrated that learners lack knowledge and confidence in inpatient insulin management. To improve the way learners are taught about inpatient insulin titration, we sought to elucidate what contextual factors and mechanisms of an educational intervention are more likely to improve glycemic outcomes, and what factors, both conscious and subconscious, learners consider when making decisions about insulin titration. To determine the contextual factors and mechanisms of successful diabetes educational interventions, we conducted a realist synthesis. After analysing 21 studies, we found that interventions that improve the insulin prescribing process are necessary but not sufficient to improve glycemic outcomes. In-person, group, prescriber-specific interventions with a reinforcing intervention are more likely to be successful. To study the factors that learners consider when titrating insulin, we conducted a mixed methods study using a case-based survey loosely modelled on script concordance testing. We found that medical students prescribed insulin with greater concordance with staff endocrinologists than residents, yet residents had more confidence than medical students in their prescribing practices. Overall, the residents prescribed larger doses of insulin. The residents were susceptible to the subconscious influence of the seniority of the nurse asking for an insulin dose, yet the medical students were not. Both groups of learners prescribed more insulin in the presence of ketones and less when the patient had hypoglycemia unawareness. Residents prescribed less insulin when patients had T2DM compared to T1DM and medical students prescribed more when the blood sugar crossed the 20mmol/L threshold. We concluded that patient, prescriber, and systems factors ultimately influenced insulin prescribing practices. Overall, we concluded that a successful educational intervention to improve inpatient glycemic control would likely need to address prescribing practices in a prescriber-specific fashion and be followed with a reinforcing intervention. We proposed that medical students and residents may need different teaching methods to address possibly different cognitive processing methods.
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.
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 | 0.007 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".