Healthy Aging in Relation to Chronic Pain and Quality of Life in Europe
Bibliographic record
Abstract
OBJECTIVE: To undertake a review of the most recent data on the relationship between quality of life (QoL) and chronic pain, as a basis for discussions about healthy aging in Europe. METHOD: A search was conducted to obtain studies on the relationship between pain severity and QoL and intervention studies reporting both QoL and pain severity in those with chronic pain in Europe. Medline and Embase were searched for observational studies and systematic reviews from 2009 to 2011. Four further databases were searched for systematic reviews and guidance from 2005 to 2011. Update searches for observational studies and systematic reviews for the period November 2011 to January 2013 were performed on Medline and Embase. RESULTS: We identified 8 observational studies and 1 systematic review that generally showed a statistically significant relationship between pain severity and QoL. We identified 5 systematic reviews of interventions in chronic pain that summarized both pain and QoL data that generally showed both a statistically significant reduction in pain and statistically significant increase in QoL. CONCLUSION: There is strong evidence of a correlation between pain severity and QoL. There is some evidence that treatment in chronic pain patients can reduce pain and simultaneously improve QoL. Prevention and treatment of chronic pain may be of significant help in reaching the aim to increase the healthy lifespan.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".