Pain among the oldest old in community and institutional settings
Bibliographic record
Abstract
The relationship between pain and increasing age was investigated using data from two different care settings collected on a province-wide basis in Ontario. Home care clients (HC) and complex continuing care patients (CCC) are assessed using the Resident Assessment Instrument-Home Care and Resident Assessment Instrument 2.0 instruments, respectively, as part of normal clinical practice. For this study, the sample was restricted to those aged 65 years and older and totaled 193,158 individuals. Centenarians (those 100 years of age or older) made up 0.41% (n=788) of the sample. Pain was assessed according to a previously validated pain scale embedded in both assessments that uses items on frequency and intensity. Based on 5-year age groups beginning at 65, the mean reported pain score was lower with each increment in age for men and women. Multiple logistic regression models were constructed and the odds ratios for pain in both HC and CCC groups decreased consistently in higher age groups after adjusting for disease diagnoses, cognition, functional status and health indicators. A model that included categories of analgesic medications coded based on the WHO pain ladder showed the relationship persisted after controlling for analgesia. In clinical settings, the oldest old appear to have lower levels of pain compared with the young old after adjusting for a variety of potential confounding variables.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".