What happens between visits? Adverse and potential adverse events among a low-income, urban, ambulatory population with diabetes
Bibliographic record
Abstract
BACKGROUND: Little is known about adverse events (AEs) that occur between physician visits for ambulatory chronic disease patients. An automated telephone self-management support programme for a diverse population of diabetes patients was implemented to capture AEs, describe the self-management domains from which they emanate and explore contributing causes. METHODS: AEs and potential AEs (PotAEs) were identified among 111 ethnically diverse diabetes patients. An AE is an injury that results from either medical management or patient self-management; a PotAE is an unsafe state likely to lead to an event if it persists without intervention. Medical record reviews were conducted to ascertain which self-management domain was involved with the event and to explore contributing causes. RESULTS: Among the 111 patients, 86% had at least one event detected over the 9-month observation period. 111 AEs and 153 PotAEs were identified. For all events, medication management was the most common domain (166 events, 63%). Only 20% of events reflected a single contributing cause; in the remaining 80%, a combination of system, clinician and patient factors contributed to their occurrence. Patient actions were implicated in 205 (77%) events, systems issues in 183 (69%) events and inadequate physician-patient communication in 155 (59%) events. Aside from communication, primary care clinician actions contributed to the occurrence of the event in only 16 cases (6%). CONCLUSIONS: Our findings reveal a complex safety ecology, with multiple contributing causes for AEs and PotAEs among ambulatory diabetes patients. Moreover, patients themselves seem to be key drivers of safety and of AEs, suggesting that patient-level self-management support and patient-centred communication are critical to AE prevention.
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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 | 0.001 | 0.000 |
| 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".