Medical characteristics of the oldest old: retrospective chart review of patients aged 85+ in an academic primary care centre
Bibliographic record
Abstract
BACKGROUND: The population aged 85 + - the "oldest old" - is now the fastest growing age segment in Canada. Although existing research demonstrates high health services utilization and medication burden in this population, little clinically derived evidence is available to guide care. This is a descriptive study in a primary care context seeking to describe the most common health conditions and medications used in the "oldest old". METHODS: We conducted a retrospective chart review of all family practice patients aged 85+ (N = 564; 209 males, 355 females) at Sunnybrook Health Sciences Centre in Toronto, Canada. Electronic medical records were reviewed for all current chronic conditions and medication prescriptions, and then stratified by sex and age subgroup (85-89, 90-94, 95+) for descriptive analysis. RESULTS: On average, patients experienced 6.4 concurrent chronic conditions and took 6.8 medications. Most conditions were related to cardiovascular (79%) and bone health (65%). Hypertension (65%) was the most common condition. Bone-related conditions (e.g. osteoarthritis, osteoporosis) and hypothyroidism predominantly affected women, while coronary artery disease and type 2 diabetes were more prevalent in men. The top two prescribed medications were atorvastatin (33%) and aspirin 81 mg (33%). Males were more likely to be prescribed lipid-lowering medications, while females were more likely to receive osteoporosis therapy. Patients received less lipid-lowering therapy with increasing age. CONCLUSIONS: Multimorbidity and polypharmacy are highly prevalent in patients in the 85+ age group. The most common clinical conditions are related to cardiovascular and bone health, and the most commonly prescribed medications are directed towards risk factors for these illnesses. In the absence of data to guide clinical decision-making, this study provides a first look at the common health concerns and medication profiles in this population and reveals trends that give rise to reflections on how clinical care for these patients can be improved.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".