The problem of pain in old age.
Bibliographic record
Abstract
The elderly are more susceptible to feeling pain than young people. Pain is described as a complex, subjective feeling causing significant limitation of physical, psychical and social functioning. In the literature, there are many classifications of pain. Considering the duration, pain may be divided into acute and chronic. Acute pain does not depend on age and lasts less than three months whereas chronic pain is more frequent with the elderly and lasts more than three months. It can be divided into nociceptive and neuropathic pain. Involutional changes progressing in the organism of an old person, combined with numerous chronic diseases occurring in old age, cause approx. 85% of the elderly to suffer from pain. Among the diseases with concurring pain, the first are diseases of the locomotor system and include: osteoporosis, osteoarthritis, and rheumatoid arthritis. Moreover, pain is an intrinsic part of malignant cancer, neuralgia shingles, and diabetic neuropathy. Pain also conceals depression and the depression intensifies the feeling of pain. Due to frequent cognitive disorders and depression, the measurement of pain in the elderly is difficult, it thus requires vast experience. To assess the pain intensity, subjective scales are used, e.g. verbal scale, score scale. To assess the qualitative and quantitative scales the following questionnaires are used: McGill-Melzak Pain Questionnaire and the Pain Assessment Form. Significant for pain assessment with people diagnosed with dementia are objective pain symptoms, namely: worsening appetite, gnashing teeth, grimaces. Pain treatment should be multimodal and include usage of both pharmacological and non-pharmacological methods. Pharmacology is the basis for pain treatment in people of old age, which should be used in the least invasive way, starting with small dosages. The pain-relieving medicine of first choice is paracetamol. In the pharmacological treatment of old people there are also non-opioid pain relieving medicine, opioids and supportive medicine. Among non-pharmacological treatments are rehabilitation and psychological therapy. In pain treatment, awareness among the elderly, their families and carers, and medical staff that the pain is not an attribute of old age; thus, it can be correctly diagnosed and treated.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".