Clinical Inertia in Response to Inadequate Glycemic Control
Bibliographic record
Abstract
OBJECTIVE: Diabetic patients with inadequate glycemic control ought to have their management intensified. Failure to do so can be termed "clinical inertia." Because data suggest that specialist care results in better control than primary care, we evaluated whether specialists demonstrated less clinical inertia than primary care physicians. RESEARCH DESIGN AND METHODS: Using administrative data, we studied all non-insulin-requiring diabetic patients in eastern Ontario aged 65 or older who had A1c results >8% between September 1999 and August 2000. Drug intensification was measured by comparing glucose-lowering drug regimens in 4-month blocks before and after the elevated A1c test and was defined as 1) the addition of a new oral drug, 2) a dose increase of an existing oral drug, or 3) the initiation of insulin. Propensity score-based matching was used to control for confounding between groups. RESULTS: There were 591 patients with specialist care and 1,911 with exclusively primary care. In the matched cohorts, 45.1% of patients with specialist care versus 37.4% with primary care had drug intensification (P = 0.009). Most of this difference was attributed to specialists' more frequent initiation of insulin in response to elevated A1c. CONCLUSIONS: Fewer than one-half of patients with high A1c levels had intensification of their medications, regardless of specialty of their physician. Specialists were more aggressive with insulin initiation than primary care physicians, which may contribute to the lower A1c levels seen with specialist care. Interventions assisting patients and physicians to recognize and overcome clinical inertia should improve diabetes care in the population.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".