Patients’ and Caregivers’ Attitudes Toward Deprescribing in Singapore
Bibliographic record
Abstract
In the article “Patients’ and Caregivers’ Attitudes Toward Deprescribing in Singapore,” the author listing and affiliations have been updated to reflect additional authors and their affiliations. The complete listing is: Chong-Han Kua, MClinPharm,1,2,* Emily Reeve, PhD,3,4 Doreen S. Y. Tan, PharmD,1,5 Tsingyi Koh, PharmD,1,6 Jie Lin Soong, PharmD,1,7 Marvin J. L. Sim, B.Sc(Pharm)(Hons.),1,8 Tracy Y. Zhang, B.Sc(Pharm)(Hons.),1,9 Yi Rong Chen, B.Sc(Pharm)(Hons.),1,10 Vanassa Ratnasingam, MBBS,11 Vivienne S. L. Mak, PhD,12 and Shaun Wen Huey Lee, PhD,2,13 for the Pharmaceutical Society of Singapore Deprescribing Workgroup1 1Pharmaceutical Society of Singapore (PSS) Deprescribing Workgroup, Singapore. 2School of Pharmacy, Monash University Malaysia, Bandar Sunway, Selangor, Malaysia. 3Geriatric Medicine Research, Faculty of Medicine and College of Pharmacy, Dalhousie University and Nova Scotia Health Authority, Halifax, Canada. 4College of Medicine, University of Saskatchewan, Saskatoon, Canada. 5Khoo Teck Puat Hospital, Singapore. 6National University Hospital, Singapore. 7Singapore General Hospital, Singapore. 8National Healthcare Group Pharmacy, Singapore. 9Ang Mo Kio-Thye Hua Kwan Hospital, Singapore. 10Tan Tock Seng Hospital, Singapore. 11Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia, Bandar Sunway, Selangor, Malaysia. 12Faculty of Pharmacy and Pharmaceutical Sciences, Monash University, Melbourne, Victoria, Australia. 13School of Pharmacy, Taylor’s University Lakeside Campus, Subang Jaya, Selangor, Malaysia. *Address correspondence to: Chong-Han Kua, MClinPharm, School of Pharmacy, Monash University Malaysia, Jalan Lagoon Selatan, 47500 Bandar Sunway, Selangor Darul Ehsan, Malaysia. E-mail: [email protected] This has been updated in the original article. Additional Acknowledgements have also been added.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".