Potentially Inappropriate Prescribing and Potential Prescribing Omissions in 82,935 Older Hospitalised Adults: Association with Hospital Readmission and Mortality within Six Months
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Polypharmacy with "potentially inappropriate medications" (PIMs) and "potential prescribing omissions" (PPOs) are frequent among those 65 and older. We assessed PIMs and PPOs in a retrospective study of 82,935 patients ≥ 65 during their first admission in the period March 2013 through February 2018 to the four acute-care Calgary hospitals. We used the American Geriatric Society (AGS) and STOPP/START criteria to assess PIMs and PPOs. We computed odds ratios (ORs) for key outcomes of concern to patients, their families, and physicians, namely readmission and/or mortality within six months of discharge, and controlled for age, sex, numbers of medications, PIMs, and PPOs. For readmission, the adjusted OR for number of medications was 1.09 (1.09-1.09), for AGS PIMs 1.14 (1.13-1.14), for STOPP PIMs 1.15 (1.14-1.15), for START PPOs 1.04 (1.02-1.06), and for START PPOs correctly prescribed 1.16 (1.14-1.17). For mortality within 6 months of discharge, the adjusted OR for the number of medications was 1.02 (1.01-1.02), for STOPP PIMs 1.07 (1.06-1.08), for AGS PIMs 1.11 (1.10-1.12), for START PPOs 1.31 (1.27-1.34), and for START PPOs correctly prescribed 0.97 (0.94-0.99). Algorithm rule mining identified an 8.772 higher likelihood of mortality with the combination of STOPP medications of duplicate drugs from the same class, neuroleptics, and strong opioids compared to a random relationship, and a 2.358 higher likelihood of readmission for this same set of medications. Detailed discussions between patients, physicians, and pharmacists are needed to improve these outcomes.
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.
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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it