Bibliographic record
Abstract
Sahar Whelan, RPh, BScPhm, MScPhm, graduated from the University of Toronto, Ontario, Canada, in 1988 with a bachelor's degree in pharmacy and in 1990 with a master's degree. She worked in the Retail and hospital sector; being the Director of Infusion Pharmacies, she specialized in compounding intravenous, nutrition, oral, dermatological, and sterile ophthalmic products. Souha Mourad, RPh, BScPhm, is a registered Pharmacist in the Province of Ontario, Canada and has more than four decades of pharmacy experience in the Middle East and Canada in areas such as hospital and retail. She specializes in the sterile and nonsterile compounding of various products for patients who require specific products not commercially available. She focuses on the customized needs of the patient. Address correspondence to Sahar Whelan, RPh, BScPhm, MScPhm, Concord Specialty Pharmacy, 2180 Steeles Avenue W, Unit 4, Concord, Ontario, Canada L4K 2Z5 (e-mail: [email protected]). The authors report no conflicts of interest. This article was written with strict ethical adherence and no funding was obtained to write the article.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".