Accuracy of Physician Billing Claims for Identifying Acute Respiratory Infections in Primary Care
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: To assess the accuracy of physician billing claims for identifying acute respiratory infections in primary care. STUDY SETTING. Nine primary care physician practices in Montreal, Canada (2002-2005). STUDY DESIGN: A validation study was carried out to compare diagnoses in 3,526 physician billing claims with diagnoses documented in the corresponding patient medical records. DATA COLLECTION: In-office medical record abstraction. PRINCIPAL FINDINGS: Claims had a high positive predictive value (PPV), negative predictive value, and specificity for identifying respiratory infections; however, their sensitivity was below 50 percent. Large variation in sensitivity and PPV was observed among physicians. CONCLUSIONS: Because claims data are now routinely used to monitor antibiotic prescribing in primary care, future research should determine if acute respiratory infection diagnoses are missing from claims at random, or if bias is present.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it