Is Chronic Nonmalignant Pain Associated with Decreased Appetite in Older Adults? Preliminary Evidence
Bibliographic record
Abstract
OBJECTIVES: To examine the association between self-reported appetite impairment and pain intensity in community-dwelling older adults with chronic nonmalignant pain. DESIGN: Cross-sectional survey. SETTING: An outpatient pain clinic at the University of Pittsburgh. PARTICIPANTS: A convenience sample of 65 older adults with chronic nonmalignant pain. MEASUREMENTS: Demographics, pain intensity (short-form McGill Pain Questionnaire), self-reported appetite impairment using a newly developed instrument, mood (30-item Geriatric Depression Scale, (GDS)), cognitive status (Folstein Mini-Mental State Examination), dependence in feeding, dependence in grocery shopping and meal preparation, and comorbidities (Cumulative Illness Rating Scale). Medication information was classified as total number of medications, number of analgesics, number of opioids, and number of potential appetite-impairing side effects. RESULTS: Univariate analyses revealed that those who reported pain-related appetite impairment had higher pain intensity than those who reported no appetite impairment (P<.001). Comparison of subjects with and without pain-related appetite impairment revealed a significant difference in GDS scores (P=.027), number of analgesics (P=.015), and number of opioids (P=.014). None of the other variables was statistically significant. The relationship between pain intensity and perceived pain-related appetite impairment was maintained in an analysis of covariance that controlled for GDS score, number of analgesics, and presence of opioids (P=.004). CONCLUSION: Chronic pain is associated with self-reported appetite impairment in older adults, but examination of the influence of reduction in pain intensity on appetite improvement is needed to establish a causal relationship between chronic pain and diminished appetite.
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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".