Chart validation of inpatient ICD-9-CM administrative diagnosis codes for ischemic stroke among IGIV users in the Sentinel Distributed Database
Bibliographic record
Abstract
The Sentinel Distributed Database (SDD) is a large database of patient-level medical and prescription records, primarily derived from insurance claims and electronic health records, and is sponsored by the U.S. Food and Drug Administration for drug safety assessments. In this chart validation study, we report on the positive predictive value (PPV) of inpatient ICD-9-CM acute ischemic stroke (AIS) administrative diagnosis codes (433.x1, 434.xx, and 436) in the SDD.As part of an assessment of the risk of thromboembolic adverse events following treatment with intravenous immune globulin (IGIV), charts were obtained for 131 potential post-IGIV AIS cases. Charts were abstracted by trained nurses and then adjudicated by stroke experts using pre-specified diagnostic criteria.Case status could be determined for 128 potential AIS cases, of which 34 were confirmed. The PPVs for the inpatient AIS diagnoses recorded in the SDD were 27% overall [95% confidence interval (95% CI): 19-35], 60% (95% CI: 32-84) for principal-position diagnoses, 42% (95% CI: 28-57) for secondary diagnoses, and 6% (95% CI: 2-15) for position-unspecified diagnoses (which in the SDD generally originate from separate physician claims associated with an inpatient stay).Position-unspecified diagnoses were unlikely to represent true AIS cases. PPVs for principal and secondary inpatient diagnosis codes were higher, but still meaningfully lower than estimates from prior chart validation studies. The low PPVs may be specific to the IGIV user study population. Additional research is needed to assess the validity of AIS administrative diagnosis codes in other study populations within the SDD.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".