Symptom clusters and quality of life among patients with advanced heart failure
Bibliographic record
Abstract
ObjectivesTo 与先进的心失败(HF ) 和与他们 .MethodsThis 是的生活(QoL ) 的质量的独立关系在病人之中识别症状簇会见了 119 个病人与的代表性的研究的第二等的数据分析在香港在一所地区性的医院的衰老老人单位推进了 HF。症状侧面和 QoL 被使用埃德蒙顿症状评价规模(ESAS ) 和 McGill QoL 问询表估计。探索因素分析被用来识别症状簇。层次回归分析被用来与他们的 QoL 检验独立关系，在调整病人们在先进年龄的年龄，性，和 comorbidities.ResultsThe 的效果以后(82.9 ±；6.5 年) 。三不同症状簇被识别：他们是悲痛簇(呼吸，焦虑，和消沉的包括的短小) ， decondition 簇(疲劳，睡意，恶心，和减少的胃口) ，和不快聚类(概括不快的疼痛，和感觉) 。这三症状簇说明了病人症状经验的 63.25% 变化。对在这些症状簇之间的中等关联小显示他们是独立人士互相。在调整年龄，性和 comorbidities 以后，悲痛(β；=−； 0.635， P <；0.001 ) ， decondition (β；=−； 0.148， P = 0.01 ) ，并且不快(β；=−； 0.258， P <；0.001 ) 症状簇独立地预言他们的 QoL.ConclusionsThis 学习与先进 HF 在病人之中识别了特殊症状簇。结果使需要清楚些为为这限制生活的疾病优化症状控制开发辩解的照顾干预。
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".