Impact of formulary policy on thiazolidinedione (TZD) use in the ontario drug benefit (ODB) program
Bibliographic record
Abstract
Intro The ODB formulary policy is very restrictive for TZD use, limiting reimbursement for second line use that must be documented in an individualized request from the prescribing physician. This study was designed to determine if patients receiving the drug met the required reimbursement criteria and whether the policy resulted in preventing appropriate patients from receiving a TZD. Methods ODB eligible type 2 DM patients using an oral hypoglycaemic drug were recruited through community pharmacies. Patients were interviewed using computer assisted telephone interviewing software with trained interviewers. Patient chart information was obtained from their physicians via a fax-back form. Results 249 patients were recruited. Mean (sd) age was 75(5) years, with 10.5 (8) years duration of DM, BMI 27.6 (5) and HgA1c 0.07 (0.01).). Hypertension prevalence 80%, hyperlipidemia 74%, cardiovascular disease 46%. 85% of TZD users had previously tried or were still using other oral hypoglycaemic drugs. Patients using a TZD were more likely to have used a diabetic clinic, had a history of an adverse effect with a non-TZD oral hypoglycaemic drug and had a greater household income than non TZD users. Conclusions The inability to access a diabetic clinic and limited household income may prevent patients who might benefit from a TZD from receiving these drugs. While restrictive formulary policies can prevent undesirable or inappropriate drug use they may prevent appropriate access as well. Clinical Pharmacology & Therapeutics (2005) 77, P72–P72; doi: 10.1016/j.clpt.2004.12.165
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".