How complete are drug history profiles that are based on public drug benefit claims?
Bibliographic record
Abstract
BACKGROUND: In Canada, programs are being developed to supply hospital emergency departments and family doctors with electronic access to their patientsâ drug history profiles. While some of these programs have access to databases that capture information about all out-patient prescriptions that are dispensed to an individual, regardless of payer; others do not, and rely upon claims paid by their provincial drug benefit plans. The completeness of these latter profiles is unknown. OBJECTIVES: To estimate the percentage of Ontario seniors who use private drug insurance (as an indicator of the potential for a âpublicâ drug history profile to be incomplete) and to describe the kinds of medications for which private insurance is used. METHODS: Cross-sectional time series analysis of Ontario Drug Benefit (ODB) claims and private drug insurance claims for Ontario residents aged 65 years or older (seniors) covering the period January 2000 to December 2005. RESULTS: During the study period, approximately 95% of Ontario seniors filled at least one prescription paid by the provincial drug benefit plan. By comparison, approximately 15-20% filled a prescription paid by a private insurer. Compared to the 20 drugs most frequently subsidized by the ODB Program (all but one of which had ODB full benefit status), the top privately-purchased drugs were more diverse: 8 had ODB full benefit status; 4 had ODB Limited Use status (which requires that patients meet prespecified clinical criteria for coverage); 3 required individual clinical review (prior authorization) by the ODB Program; and 5 were ODB non-benefits. CONCLUSIONS: Many Ontario seniors are at risk for an incomplete ODB drug history profile. Further research is needed to confirm whether this causes problems for physicians and patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.113 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".