Severe pain at the end of life: a population-level observational study
Bibliographic record
Abstract
BACKGROUND: Pain is a prevalent symptom at the end of life and negatively impacts quality of life. Despite this, little population level data exist that describe pain frequency and associated factors at the end of life. The purpose of this study was to explore the prevalence of clinically significant pain at the end of life and identify predictors of increased pain. METHODS: Retrospective population-level cohort study of all decedents in Ontario, Canada, from April 1, 2011 to March 31, 2015 who received a home care assessment in the last 30 days of life (n = 20,349). Severe daily pain in the last 30 days of life using linked Ontario health administrative databases. Severe pain is defined using a validated pain scale combining pain frequency and intensity: daily pain of severe intensity. RESULTS: Severe daily pain was reported in 17.2% of 20,349 decedents. Increased risk of severe daily pain was observed in decedents who were female, younger and functionally impaired. Those who were cognitively impaired had a lower risk of reporting pain. Disease trajectory impacted pain; those who died of a terminal illness (i.e. cancer) were more likely to experience pain than those with frailty (odds ratio 1.66). CONCLUSION: Pain is a common fear of those contemplating end of life, but severe pain is reported in less than 1 in 5 of our population in the last month of life. Certain subpopulations may be more likely to report severe pain at the end of life and may benefit from earlier palliative care referral and intervention.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".