A Validation Study of Administrative Data Algorithms to Identify Patients with Parkinsonism with Prevalence and Incidence Trends
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
BACKGROUND: Epidemiological studies for identifying patients with Parkinson's disease (PD) or Parkinsonism (PKM) have been limited by their nonrandom sampling techniques and mainly veteran populations. This reduces their use for health services planning. The purpose of this study was to validate algorithms for the case ascertainment of PKM from administrative databases using primary care patients as the reference standard. METHODS: We conducted a retrospective chart abstraction using a random sample of 73,003 adults aged ≥ 20 years from a primary care Electronic Medical Record Administrative data Linked Database (EMRALD) in Ontario, Canada. Physician diagnosis in the EMR was used as the reference standard and population-based administrative databases were used to identify patients with PKM from the derivation of algorithms. We calculated algorithm performance using sensitivity, specificity, and predictive values and then determined the population-level prevalence and incidence trends with the most accurate algorithms. RESULTS: We selected, '2 physician billing codes in 1 year' as the optimal administrative data algorithm in adults and seniors (≥ 65 years) due to its sensitivity (70.6-72.3%), specificity (99.9-99.8%), positive predictive value (79.5-82.8%), negative predictive value (99.9-99.7%), and prevalence (0.28-1.20%), respectively. CONCLUSIONS: Algorithms using administrative databases can reliably identify patients with PKM with a high degree of accuracy.
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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 | 0.000 | 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 | 0.000 | 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 it