International expert panel’s potentially inappropriate prescribing cascades (PIPC) list
Bibliographic record
Abstract
PURPOSE: Prescribing cascades contribute to potentially inappropriate prescribing, especially among older adults. With the prescribing cascade framework maturation, it is important to distinguish potentially inappropriate from potentially appropriate prescribing cascades. The objective was to create a comprehensive consensus list of Potentially Inappropriate Prescribing Cascades (PIPCs). METHODS: A prescribing cascade inventory was compiled using published lists. An international panel of 12 experts in geriatric medicine and pharmacology was selected. Panelists participated in a Delphi consensus process completing two questionnaire rounds and one discussion round. RESULTS: A total of 107 proposed prescribing cascades were identified. After Questionnaire Round 1, 56 prescribing cascades achieved a rating of agree or strongly agree by ≥ 75% of the panelists and were included in the PIPC list. For 32 of the 107 proposed cascades, 50-74% of panelists provided a rating of agree or strongly agree, and were moved to Round 2. In Questionnaire Round 2, 9 of 32 proposed cascades achieved a rating of agree or strongly agree by ≥ 75% of panelists and were included in the final PIPC list. For 14 prescribing cascades, 50-74% of panelists provided a rating of agree or strongly agree, and were included in the Discussion Round. After discussion, no additional prescribing cascades were included. CONCLUSION: An explicit list of 65 PIPCs was created using a rigorous Delphi consensus process conducted by international experts on pharmacotherapy for older adults. The PIPC list provides a crucial tool for clinicians and researchers to detect potentially inappropriate prescribing patterns and to foster efforts to improve medication safety.
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 | 0.107 | 0.156 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.012 | 0.005 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".