Agreement between patients' self‐report and medical records for vaccination: the PGRx database
Bibliographic record
Abstract
PURPOSE: Patients' self-reported vaccine exposure (PS) may be subject to memory errors and other biases. Physicians' prescription records and other medical records (MR) do not capture noncompliance with vaccination. This study compared PS with MR for influenza, 23-valent pneumococcal, and human papillomavirus (HPV) vaccines. METHODS: The Pharmacoepidemiologic General Research Extension (PGRx) database uses a network of over 300 general practitioners across France, who systematically recruit an age- and sex-stratified sample of patients (≥ 14 years old), without reference to their diagnoses or prescriptions. Patients received a structured telephone interview, combined with an interview guide listing vaccines commonly given. Patients' self-reported vaccination in the 3 years before their recruitment was compared with medical records kept by the physician or the patient. RESULTS: Concordance between PS and MR was assessed for 7613 patients for whom both sources of information were available. Agreement within 3 years before the recruitment date was substantial for influenza vaccines (prevalence and bias-adjusted kappa [PABAK] = 0.74, sensitivity PS relative to MR 81.5%) and high for 23-valent pneumococcal vaccines (PABAK = 0.98, sensitivity PS 49.6) and HPV vaccines (PABAK = 0.92, sensitivity PS 91.6). In adjusted analyses, agreement varied with sociodemographic and health-related factors, particularly for influenza and 23-valent pneumococcal vaccines. CONCLUSIONS: The PGRx method for drug exposure assessment is a new tool in pharmacoepidemiology that shows substantial to high agreement between PS and MR for exposure to various vaccines. Our finding of high agreement between PS and MR for HPV vaccination status in young women is a significant addition to the literature.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".