Who provides chronic disease management?
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
OBJECTIVE: To determine the proportions of patients who receive care from family physicians, specialists, and nurse practitioners for the management of common chronic medical conditions. DESIGN: Population-based retrospective cohort study. SETTING: Province of Alberta. PARTICIPANTS: Adults aged 19 years or older who were registered for provincial health services and each had 2 or more interactions with the same provider between January 1, 2013, and December 31, 2017, for any of 7 specified chronic medical conditions: hypertension, diabetes, chronic obstructive pulmonary disease (COPD), asthma, heart failure, ischemic heart disease, and chronic kidney disease. MAIN OUTCOME MEASURES: Numbers of patients being managed for these conditions and which provider types were involved in their care. RESULTS: Albertans receiving care for the chronic medical conditions being studied (n=970,783) had a mean (SD) age of 56.8 (16.3) years and 49.1% were female. Family physicians were the sole providers of care for 85.7% of patients with a diagnosis of hypertension, 70.9% with diabetes, 59.8% with COPD, and 65.5% with asthma. Specialists were sole providers of care for 49.1% of patients with ischemic heart disease, 42.2% with chronic kidney disease, and 35.6% with heart failure. Nurse practitioners were involved in the care of less than 1% of patients with these conditions. CONCLUSION: Family physicians were involved in the care of most patients with any of 7 chronic medical conditions included in this study and were the sole providers of care for the majority of patients with hypertension, diabetes, COPD, and asthma. Guideline working group representation and the setting of clinical trials should reflect this reality.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.009 |
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