Managing chronic diseases in the frail elderly
Bibliographic record
Abstract
Polypharmacy is a common result of managing multiple chronic diseases. While applying guidelines and adjusting medications to reach clinical targets, health care practitioners may inadvertently worsen quality of life. For example, a typical response to a high A1c level in a person with type 2 diabetes would be to increase oral hypoglycemic doses or add another medication to reach target; certainly, hypoglycemia would not be immediately suspected. Similarly, it might be difficult to think of reducing doses of heart failure medication when side effects such as hypotension are detected. Patients referred to the Bruyere Geriatric Day Hospital (GDH) for a 12-week admission and seen for medication review have an average of 9 drug-related problems, the most common of which include no longer needing a medication and suffering from an adverse effect.1 This case illustrates how addressing hypoglycemia by reducing medication use and addressing low blood pressure by reducing heart failure medications were effective in reducing fall risk. Taking steps to improve pain control, mood and sleep disturbances assisted in restoring function and ultimately improved medication adherence, diabetic control and quality of life for the patient. A description of the GDH processes and, in particular, communication about medication-related care can be found in Appendix 1 (available online at cph.sagepub.com/supplemental).
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".