Development and validation of an improving prescribing in the elderly tool.
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
OBJECTIVE: To apply recently published consensus panel guidelines to a series of hospital inpatient charts to develop and validate a brief screening tool for potentially inappropriate prescriptions in the elderly. SETTING: A 400-bed acute care hospital in London, Ontario. METHODS: Three hundred and sixty-one consecutive inpatient charts, 185 from a clinical teaching unit (CTU) and 176 from a geriatric assessment unit (GAU) were examined for potentially inappropriate prescriptions as listed by McLeod et al. The potentially inappropriate prescribing practices detected were used to develop the Improving Prescribing in the Elderly Tool (IPET). Construct validity was examined by looking for a predicted difference in the rate of potentially inappropriate prescriptions between the CTU and the GAU. Interrater reliability was determined by applying the IPET to a new series of 100 charts. RESULTS: Forty-two of 361 individuals (12.5%) had 45 potentially inappropriate prescriptions representing 14 different potential drug/disease interactions; these were used to construct the IPET. A demonstrated difference in the rate of potentially inappropriate prescriptions between the CTU and GAU indicated construct validity. The interrater reliability of the IPET (kappa) when applied to a new series of 100 charts was 1.0. INTERPRETATION: The IPET is a brief, reliable and valid tool based on the published literature that may be used to screen for potentially inappropriate prescriptions in the elderly.
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 | 0.000 |
| 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 it