Approach to preventive care in the elderly.
Bibliographic record
Abstract
OBJECTIVE: To guide family physicians in creating preventive screening and treatment plans for their elderly patients. SOURCES OF INFORMATION: The MEDLINE database was searched for Canadian guidelines on primary health care and the elderly; guidelines or meta-analyses or practice guidelines or systematic reviews related to mass screening in those aged 80 and older and the frail elderly, limited to between 2006 and July 2016; and articles on preventive health services for the elderly related to family practice or family physicians, limited to English-language publications between 2012 and July 2016. MAIN MESSAGE: Estimating life expectancy is not an easy or precise science, but frailty is an emerging concept that can help with this. The Canadian Task Force on Preventive Health Care offers cancer screening guidelines, but they are less clear for patients older than 74 years and management plans need to be individualized. Estimating remaining years of life helps guide your recommendations for preventive screening and treatment plans. Risks often increase along with an increase in frailty and comorbidity. Conversely, benefits often diminish as life expectancy decreases. Preventive management plans should take into account the patient's perspective and be mutually agreed upon. A mnemonic device for key primary care preventive areas-CCFP, short for cancer, cardiovascular disease, falls and osteoporosis, and preventive immunizations-might be useful. CONCLUSION: Family physicians might find addressing the following areas helpful when considering a preventive health intervention: age, life expectancy (including concept of frailty), comorbidities and functional status, risks and benefits of screening or treatment, and values and preferences of the patient.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".