Administrative Data Algorithms Can Describe Ambulatory Physician Utilization
Bibliographic record
Abstract
OBJECTIVE: To validate algorithms using administrative data that characterize ambulatory physician care for patients with a chronic disease. DATA SOURCES: Seven-hundred and eighty-one people with diabetes were recruited mostly from community pharmacies to complete a written questionnaire about their physician utilization in 2002. These data were linked with administrative databases detailing health service utilization. STUDY DESIGN: An administrative data algorithm was defined that identified whether or not patients received specialist care, and it was tested for agreement with self-report. Other algorithms, which assigned each patient to a primary care and specialist physician, were tested for concordance with self-reported regular providers of care. PRINCIPAL FINDINGS: The algorithm to identify whether participants received specialist care had 80.4 percent agreement with questionnaire responses (kappa=0.59). Compared with self-report, administrative data had a sensitivity of 68.9 percent and specificity 88.3 percent for identifying specialist care. The best administrative data algorithm to assign each participant's regular primary care and specialist providers was concordant with self-report in 82.6 and 78.2 percent of cases, respectively. CONCLUSIONS: Administrative data algorithms can accurately match self-reported ambulatory physician utilization.
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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 | 0.032 | 0.228 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".