A record review of reported musculoskeletal pain in an Ontario long term care facility
Bibliographic record
Abstract
BACKGROUND: Musculoskeletal (MSK) pain is one of the leading causes of chronic health problems in people over 65 years of age. Studies suggest that a high prevalence of older adults suffer from MSK pain (65% to 80%) and back pain (36% to 40%). The objectives of this study were: 1. To investigate the period prevalence of MSK pain and associated subgroups in residents of a long-term care (LTC) facility. 2. To describe clinical features associated with back pain in this population. 3. To identify associations between variables such as age, gender, cognitive status, ambulatory status, analgesic use, osteoporosis and osteoarthritis with back pain in a long-term care facility. METHODS: A retrospective chart review was conducted using a purposive sampling approach of residents' clinical charts from a LTC home in Toronto, Canada. All medical records for LTC residents from January 2003 until March 2005 were eligible for review. However, facility admissions of less than 6 months were excluded from the study to allow for an adequate time period for patient medical assessments and pain reporting/charting to have been completed. Clinical data was abstracted on a standardized form. Variables were chosen based on the literature and their suggested association with back pain and analyzed via multivariate logistic regression. RESULTS: 140 (56%) charts were selected and reviewed. Sixty-nine percent of the selected residents were female with an average age of 83.7 years (51-101). Residents in the sample had a period pain prevalence of 64% (n = 89) with a 40% prevalence (n = 55) of MSK pain. Of those with a charted report of pain, 6% (n = 5) had head pain, 2% (n = 2) neck pain, 21% (n = 19) back pain, 33% (n = 29) extremity pain and 38% (n = 34) had non-descriptive/unidentified pain complaint. A multivariate logistic regression analysis revealed that osteoporosis was the only significant association with back pain from the variables studied (P = 0.001). CONCLUSION: Residents with back pain represent 13.6% (n = 19) of the sample population studied. This is as frequent as other serious conditions commonly found in LTC. Of the variables studied, only osteoporosis and the self-report of back pain were found to be associated. The back pain resident in this facility can typically be described as female, osteoporotic, with mild to moderate dementia, an independent or assisted walker having low levels of depression. Further research using other sites is needed to determine the overall prevalence of this condition and its impact on quality of life issues. The results of this study should inform future research in this area.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".