Sarcopenia-A baby boomers dilemma for nurse practitioners to discover, diagnose, and treat
Bibliographic record
Abstract
Objective: Sarcopenia is a disease of low skeletal muscle mass and strength that occurs with aging. It is most commonly seen in individuals aged 50 years and over. Nurse practitioners can take a proactive approach to the understanding and screening of this disease in attempts to prolong its onset or to treat the condition before it leads to additional adverse consequences. Methods: A comprehensive review of the literature, including evidence-based literature from peer-reviewed articles, including randomized controlled trials, was conducted.Results: This review of the literature indicated patients can benefit greatly from nurse practitioner’s awareness and intervention by screening for sarcopenia as well as offering appropriate education and treatment to their patients. Once a diagnosis is reached, the nurse practitioner can then collaborate with other disciplines such as nutrition, medicine, exercise physiology and/or physical therapy to develop an intervention strategy that can treat or prevent this condition before it leads to decreased independence, early onset disability and decreased quality of life, among other adverse health outcomes.Conclusions: There is a call to action on the part of nurse practitioners in efforts to prevent and/or slow the onset of age-related sarcopenia and its adverse consequences.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.012 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.012 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 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".