Validation of Diagnostic Groups Based on Health Care Utilization Data Should Adjust for Sampling Strategy
Bibliographic record
Abstract
OBJECTIVE: Valid measurement of outcomes such as disease prevalence using health care utilization data is fundamental to the implementation of a "learning health system." Definitions of such outcomes can be complex, based on multiple diagnostic codes. The literature on validating such data demonstrates a lack of awareness of the need for a stratified sampling design and corresponding statistical methods. We propose a method for validating the measurement of diagnostic groups that have: (1) different prevalences of diagnostic codes within the group; and (2) low prevalence. METHODS: We describe an estimation method whereby: (1) low-prevalence diagnostic codes are oversampled, and the positive predictive value (PPV) of the diagnostic group is estimated as a weighted average of the PPV of each diagnostic code; and (2) claims that fall within a low-prevalence diagnostic group are oversampled relative to claims that are not, and bias-adjusted estimators of sensitivity and specificity are generated. APPLICATION: We illustrate our proposed method using an example from population health surveillance in which diagnostic groups are applied to physician claims to identify cases of acute respiratory illness. CONCLUSIONS: Failure to account for the prevalence of each diagnostic code within a diagnostic group leads to the underestimation of the PPV, because low-prevalence diagnostic codes are more likely to be false positives. Failure to adjust for oversampling of claims that fall within the low-prevalence diagnostic group relative to those that do not leads to the overestimation of sensitivity and underestimation of specificity.
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How this classification was reachedexpand
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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".