Abstracts from the 33rd Annual Scientific Meeting of the Canadian Geriatrics Society Toronto, April 2013
Bibliographic record
Abstract
Background/Purpose: The 85+-year-old population -the "oldest old" -is now the fastest growing age segment in Canada.Although existing research demonstrates high health services utilization and prescribed medications in this population, little epidemiological evidence is available to guide care for this age group.Objective: To describe the epidemiological characteristics of common health conditions and medication prescriptions in the "oldest old". Methods:We conducted a retrospective chart review of all family practice patients aged ≥ 85 (N = 564; 209M:355F) at Sunnybrook Health Sciences Centre, Toronto.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.Bonerelated conditions (e.g., osteoarthritis, osteoporosis) and hypothyroidism predominantly affected women (p < .001),while coronary artery disease and T2DM were more prevalent in men (p < .05).The top two prescribed medications were Atorvastatin (33%) and ASA 81 mg (33%).Males were more likely to be prescribed lipid-lowering medications, while females were more likely to receive osteoporosis therapy (p < .001).Patients received less lipid-lowering therapy with increasing age (p < .001).Discussion & Conclusion: Multimorbidity and polypharmacy are highly prevalent in patients over 85.Most clinical concerns in this population relate to cardiovascular and bone health; medications predominantly treat risk factors.In the absence of epidemiological data to guide clinical decisionmaking, this study provides a first look at the common health concerns and medication profiles in this population.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.268 | 0.078 |
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".