Relationship between GP visits and time spent in-hospital among insulin-dependent Canadians with type 2 diabetes.
Bibliographic record
Abstract
OBJECTIVE: To determine whether higher frequency of GP visits among insulin-dependent patients with type 2 diabetes is associated with reduced hospitalizations. DESIGN: Nationally representative study using data from the 2013-2014 cycle of the Canadian Community Health Survey. SETTING: Canada. PARTICIPANTS: A study sample comprising 2203 insulin-dependent Canadians with type 2 diabetes. MAIN OUTCOME MEASURES: The relationship between GP visits (no visits, 1-5 visits, ≥ 6 visits) in the past year and the number of nights spent in-hospital. Zero-inflated negative binomial Poisson regression models were used to account for overdispersion and excess zeros. RESULTS: Higher numbers of GP visits were associated with spending fewer nights in-hospital. Patients with 1 to 5 GP visits had a significantly lower number of nights spent in-hospital (rate ratio of 0.38, 95% CI 0.25 to 0.56), as did those with 6 or more GP visits (rate ratio of 0.57, 95% CI 0.38 to 0.84) despite having reduced odds of not being hospitalized (odds ratio of 0.62, 95% CI 0.39 to 0.95), compared with those who did not see a GP in the past year, after adjusting for confounders. CONCLUSION: We found that insulin-dependent patients with diabetes who saw GPs more frequently were hospitalized less commonly compared with those who did not see a GP in the past year. Further research is needed to examine relationships with other types of follow-up, ideally using a longitudinal design.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".