Quality of life and its association with direct medical costs for COPD in urban China
Bibliographic record
Abstract
BACKGROUND: Chronic obstructive pulmonary disease (COPD) is one of the leading causes of death worldwide. Few studies have focused on the quality of life (QoL) associated medical costs for COPD in China. METHODS: A cross-sectional survey of 678 COPD patients was conducted in four major cities (Beijing, Shanghai, Guangzhou and Chengdu), China, in 2011. Data on socio-demographic information, health conditions and medical costs were collected through a face-to-face interview combined with medical record searching. The EuroQol (EQ-5D) health questionnaire was applied to assess the QoL of COPD patients. RESULTS: Among 678 patients with COPD, nearly 40% had difficulties in mobility, usual activities and pain/discomfort, one third had various degrees of anxiety/depression, and one fifth had difficulties in self-care. The COPD patients had a median utility score of 0.768 and a median visual analog scale score of 70. The degree of difficulties in any dimensions significantly increased, and utility and health scores decreased with severity of the disease. Age, gender and disease severity were significantly associated with the quality of life after taking other covariates into consideration. Poorer QoL was a significant indicator of higher direct medical costs for COPD patients. CONCLUSION: Impaired quality of life was significantly linked to increased medical costs for COPD patients and could be an important measure for policy- and decision-making in COPD care.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".