Frailty: Identifying elderly patients at high risk of poor outcomes.
Bibliographic record
Abstract
OBJECTIVE: To help family physicians better recognize frailty and its implications for managing elderly patients. SOURCES OF INFORMATION: PubMed-MEDLINE was searched from 1990 to 2013. The search was restricted to English-language articles using the following groups of MeSH headings and key words: frail elderly, frail, frailty; aged, geriatrics, geriatric assessment, health services for the aged; and primary health care, community health services, and family practice. MAIN MESSAGE: Frailty is common, particularly in elderly persons with complex chronic conditions such as heart failure and chronic obstructive pulmonary disease. Emerging evidence demonstrates the value of frailty as a predictor of adverse outcomes in older persons. While there is currently a lack of consensus as to how best to assess and diagnose frailty in primary care practice, individual markers of frailty such as low gait speed offer a promising feasible means of screening for frailty. Identification of frailty in primary care might provide an opportunity to delay the progression of frailty through proactive interventions such as exercise, and awareness of frailty can guide appropriate counseling and anticipatory preventive measures for patients when considering medical interventions. Recognition of frailty might also help identify and optimize the management of coexisting conditions that might contribute to or be affected by frailty. Further research should be directed at identifying feasible and effective ways to appropriately assess and manage these vulnerable patients at the primary care level. CONCLUSION: Despite its importance, little attention has been given to the concept of frailty in family medicine. Frailty is easily overlooked because its manifestations can be subtle, slowly progressive, and thus dismissed as normal aging; and physician training has been focused on specific medical diseases rather than overall vulnerability. For primary care physicians, recognition of frailty might help them provide appropriate counseling to patients and family members about the risks of medical interventions.
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How this classification was reachedexpand
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 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".