Status of pain,negative emotion,sleeping, and quality of life in patients with osteosarcoma
Bibliographic record
Abstract
Objective To determine the status of pain, negative emotion, sleeping, and quality of life in patients with osteosarcoma and the correlations among them. Methods Pain, emotion, sleeping, and quality of life in 139 patients with osteosarcoma and 139 healthy controls were evaluated by Short-form McGill Pain Questionnaire (SF-MPQ), Pain Self-efficacy Questionaire (PSEQ), Beck Depression Inventory (BDI), Beck Anxiety Inventory (BAI), Athens insomnia scale (AIS), and 12-item Short Form Health Survey (SF-12). Results Sixty-one point two percent patients with osteosarcoma showed from moderate to severe pain intensity, and there was a negative correlation between PSEQ score and pain intensity (P0.001). Comparing with the healthy controls, the osteosarcoma patients reported higher scales scores of emotion, sleeping, and quality of life (P0.01). There were significant correlations among the scale scores of pain, emotion, sleeping, and quality of life (P0.05). Conclusion Osteosarcoma causes pain, negative emotions, and the problems of for sleeping which negatively impacts the quality of patients’ life.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".