Managing Pain in Chronically Ill Homebound Patients Through Home-Based Primary and Palliative Care
Bibliographic record
Abstract
BACKGROUND:: Many older adults are homebound due to chronic illness and suffer from significant symptoms, including pain. Home-based primary and palliative care (HBPC), which provides interdisciplinary medical and psychosocial care for this population, has been shown to significantly reduce symptom burden. However, little is known about how pain is managed in the homebound. OBJECTIVE:: This article describes pain management for chronically, ill homebound adults in a model, urban HBPC program. DESIGN/MEASUREMENTS:: This was a prospective observational cohort study of newly enrolled HBPC patients, who completed a baseline Edmonton Symptom Assessment System (ESAS) survey during the initial HBPC visit (N = 86). Baseline pain burden was captured by ESAS and pain severity was categorized as none, mild, or moderate-severe. All pain-related assessments and treatments over a 6-month period were categorized by medication type and titration, referrals to outside providers, procedures, and equipment. RESULTS:: At baseline, 55% of the study population had no pain, 18% had mild pain, and 27% had moderate-severe pain. For those with moderate-severe pain at baseline (n = 23), prescriptions for pharmacological treatments for pain, such as opiates and acetaminophen, increased during the study period from 48% to 57% and 52% to 91%, respectively. Nonpharmacological interventions, including referrals to outside providers such as physical therapy, procedures, and equipment for pain management, were also common and 67% of the study population received a service referral during the follow-up period. CONCLUSIONS:: Pharmacological and nonpharmacological treatments are widely used in the setting of HBPC to treat the pain of homebound, older adults.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".