Quality of Diabetes Care Among the Canadian Regular Forces: A Retrospective Cohort Study
Bibliographic record
Abstract
The objective of the thesis was to evaluate the quality of diabetes care in the Canadian Forces by determining the extent to which physicians adhere to recommendations outlined in the 2008 Canadian Diabetes Association (CDA) clinical practice guidelines. In addition, the effect of patient age, sex, rank and size of base on quality of care was assessed and the accuracy of a diagnosis of diabetes in an extract of the electronic medical record (EMR) was evaluated. Fourteen bases within the Canadian Forces were selected for investigation, representing roughly half of the Canadian Forces population. Cases of diabetes were ascertained based on laboratory criteria following a chart review. Twenty-one CDA guideline recommendations were considered. The Canadian Forces demonstrated greater than 75% adherence with each of 9 recommendations, 50-75% adherence with each of 7 recommendations and less than 50% adherence with each of 5 recommendations. The overall adherence with all applicable recommendations per patient was 60.3% (SE 0.66). Age, sex, rank and size of base were not important factors influencing guideline adherence. The sensitivity of a diabetes diagnosis in an extract of the EMR was 84.5%, the specificity was 99.8%, the positive predictive value was 85.1% and the negative predictive value was 99.8%. This is similar to the performance of provincial and national diabetes registries. The quality of diabetes care in the Canadian Forces compared favourably with that of the civilian population within Canada and internationally. The creation of a diabetes registry is expected to lead to further improvements in diabetes care.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".