Evaluation of a clinic-based quality structure for Special Access Programme medicines to treat parasitic infections
Bibliographic record
Abstract
Background: Frequently used drugs in our unit are only available through Health Canada’s Special Access Programme (SAP) or a compounding pharmacy. A tracking system was implemented to evaluate the turnaround time (TAT) and success rate of SAP applications for parasitic infections. Methods: We undertook a retrospective review of SAP logs from 2013 to 2015 inclusive, with outcomes of TAT and initial application success rates over time. Analyses were stratified by drug indication. Results: The mean TATs for all indications from 2013 to 2015 were 9.02 (SD 10.11) days, 7.04 (SD 7.6) days, and 7.25 (SD 8.97) days, respectively (p = 0.48). First-time success rates for ivermectin from 2013 to 2015 were 96%, 84%, and 71%, respectively. First-time success rates for albendazole from 2013 to 2015 were 74%, 60%, and 63%, respectively. In 2013, 14% (6/44) of initial SAP requests received an incomplete notification compared with 25% (14/57) and 32% (25/78) in 2014 and 2015, respectively (p = 0.08). Conclusions: Timely initiation of antihelminthic therapy is critical to reducing the risk of adverse clinical outcomes and a decreased quality of life from parasitic infections such as strongyloidiasis. Our findings document a prolonged TAT of non-formulary medications used to treat common helminthiases in Canada.
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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 | 0.032 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".