The case for continuity of care for people with chronic obstructive pulmonary disease
Bibliographic record
Abstract
Introduction Continuity of care by family physicians in primary care settings may play a role in reducing health resource utilization and improving clinical outcomes and satisfaction of patients with chronic obstructive pulmonary disease. Clear evidence on the impact of continuity of care will support clinical programing and integration of services across health settings. Methods The association between continuity of care and unplanned health service utilization in persons with a diagnosis of chronic obstructive pulmonary disease in a rural region in Ontario, Canada was evaluated. A retrospective cohort study was conducted using population-level health administrative data. The main exposure variable was continuity of care. Results A continuity of care index was calculated for patients with at least five visits to a healthcare provider during the 5-year follow-up period ( n = 40,033). Higher continuity of care ( n = 20,008) and lower continuity of care ( n = 20,025), based on the median continuity of care score were calculated. Patients with lower continuity of care had an increased adjusted relative risk of 2.12 (2.08, 2.33) of an emergency department visit, 2.81 (2.72, 2.9) risk of hospitalization, and 3.52 (3.24, 3.82) of being readmitted to hospital compared to those with higher continuity of care. Discussion An association between continuity of care and unplanned health services utilization, where a lower use of unplanned health services was observed in the cohort of patients with chronic obstructive pulmonary disease experiencing higher continuity of care. Continuity of care makes philosophical and social sense in that care is provided by a known provider to a known patient and unnecessary investigations can be avoided.
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 | 0.022 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.009 | 0.021 |
| Insufficient payload (model declined to judge) | 0.008 | 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".