Utilization of Health Care Resources by Long-term Care Residents as a Function of Pain Status
Bibliographic record
Abstract
OBJECTIVE: We estimated the association between the presence of pain and health care utilization among older adults residing in long-term care (LTC) facilities. MATERIALS AND METHODS: Using administrative health data maintained by the Saskatchewan ministry of health and time-to-event analyses with multivariable frailty models, we tested for differences in health care use (hospitalization, physician and specialist visits, and prescription drug dispensations) as a function of pain status among LTC residents after admission to an LTC. Specifically, we contrasted LTC residents with daily pain or less than daily pain but with moderate or severe intensity (ie, clinically significant pain group; CSP) to residents with no pain or nondaily mild pain (NP/NDMP group). RESULTS: Our cohort consisted of 24,870 Saskatchewan LTC residents between 2004 and 2015 with an average age of 85 years (63.2% female; 63.0% in urban facilities). Roughly one third had CSP at their LTC admission date. Health care use after admission to LTC was strongly associated with pain status, even after adjusting for residents' demographic and facility characteristics, prior comorbidities and health care utilization 1 year before the study index date. In any given quarter, compared with NP/NDMP residents, those with CSP had an increased risk of hospitalization, specialist visit, follow-up general practitioner visit, and onset of polypharmacy (ie, 3 or more medication classes). DISCUSSION: To our knowledge, this is the first large-scale project to examine the utilization of health care resources as a function of pain status among LTC facility residents. Improved pain management in LTC facilities could lead to reduced health care use.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".