Bibliographic record
Abstract
Unintentional weight loss in people older than 65 years is associated with increased morbidity and mortality. Nonmalignant diseases are more common causes of unintentional weight loss in this population than malignant causes. However, malignancy accounts for up to one-third of cases of unintentional weight loss. Medication use and polypharmacy can interfere with the sense of taste or induce nausea and should not be overlooked as causative factors. Social factors such as isolation and financial constraints may contribute to unintentional weight loss. A readily identifiable cause is not found for 6% to 28% of cases. Recommended tests include age-appropriate cancer screenings, complete blood count, basic metabolic panel, liver function tests, thyroid function tests, C-reactive protein level, erythrocyte sedimentation rate, lactate dehydrogenase measurement, ferritin, protein electrophoresis, and urinalysis. Chest radiography and fecal occult blood testing should be performed. Further imaging and invasive testing may be considered based on initial evaluation. When the initial evaluation is unremarkable, a three- to six-month observation period is recommended with follow-up based on clinician and patient preferences. Treatment should focus on the underlying cause if known. Dietary modifications that consider patient preferences and chewing or swallowing disabilities should be considered. Appetite stimulants and high-calorie supplements are not recommended. Treatment should focus on feeding assistance, addressing contributing medications, providing appealing foods, and social support.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".