Bibliographic record
Abstract
目的：探讨骨肉瘤患者的疼痛、负性情绪、睡眠和生活质量及其相互关系。方法：应用简式McGill疼痛问卷（SF-MPQ）、疼痛自我效能感问卷（PSEQ）、贝克抑郁量表（BDI）、贝克焦虑量表（BAI）、阿森斯失眠量表（AIS）、简式健康相关生活质量问卷（SF-12）对139名骨肉瘤患者和139名健康者的疼痛、情绪、睡眠和生活质量进行评估。结果：61.2%的骨肉瘤患者有中等以上强度的疼痛,PSEQ分与疼痛强度呈明显负相关（P〈0.001）;骨肉瘤组在情绪、睡眠、生活质量等量表上的得分均明显高于正常对照组（P〈0.01）;骨肉瘤组在疼痛、情绪、睡眠、生活质量等量表上的得分之间存在相关关系（P〈0.05）。结论：骨肉瘤导致患者存在疼痛、抑郁、焦虑和睡眠问题,且这些问题相互影响,使得骨肉瘤患者生活质量降低。
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".