Coding accuracy of administrative drug claims in the Ontario Drug Benefit database.
Bibliographic record
Abstract
BACKGROUND: Every year in Ontario, the records of over 42 million prescriptions dispensed to persons eligible for Ontario Drug Benefit (ODB) benefits are transmitted to a central database. The ODB database is the second largest database of medications in Canada, containing records on almost half of all medications dispensed in Ontario. There is no information about the reliability of the coding on the ODB drug claims database and, therefore, the objective of this study was to estimate the reliability of coding of the Drug Identification Number, and the date, quantity and duration of the dispensation on claims sent to the ODB. METHODS: To meet this objective, approximately 100 randomly selected prescriptions dispensed from each of 50 pharmacies in southern Ontario between July 1, 1998 and December 31, 1999 were audited. For each claim, the written information on the prescription was compared with the electronic information submitted to the ODB database. Logistic regression was used to test the association between coding errors and the location, owner affiliation, and productivity of each pharmacy (defined as the annual volume of dispensations divided by the annual number of hours worked by all pharmacists and pharmacy assistants). RESULTS: Of the 183 pharmacies owners invited to participate, consent to abstract information was obtained in 50, yielding a participation rate of 27%. Of the 5155 dispensed prescriptions, 37 errors were found, yielding an overall error rate of 0.7% (95% CI 0.5% to 0.9%). None of the characteristics of pharmacies that were examined (location, owner affiliation, productivity) was associated with coding errors. CONCLUSIONS: Pharmacists almost always dispense the medication that is prescribed and this information is reliably transmitted to the ODB drug claims database. This means that any conclusions drawn by researchers using these data are not likely to be compromised by low coding reliability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 | 0.145 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".