doi:10.1155/2012/851276 Research Article Depression and Anxiety Symptoms Relate to Distinct Components of Pain Experience among Patients with Breast Cancer
Bibliographic record
Abstract
Copyright © 2012 Sarah K. Galloway et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Breast cancer is a leading cancer diagnosis among women worldwide, with more than 210,000 new cases and 40,000 deaths per year in the United States. Pain, anxiety, and depression can be significant factors during the course of breast cancer. Pain is a complex experience with sensory, affective, and cognitive dimensions. While depression and anxiety symptoms are relatively common among breast cancer patients, little is known about the relation between these psychiatric factors and distinct components of the pain experience. In the present study 60 females presenting to an NCI-designated Cancer Center with newly diagnosed breast cancer completed the Center for Epidemiological Studies 10-item Depression Scale, the State Instrument of the Spielberger State-Trait Anxiety Inventory, and the McGill Pain Questionnaire. Findings indicate that anxiety and depression are common among newly diagnosed breast cancer patients; furthermore, patients experience an appreciable amount of pain even before oncologic treatment starts. State anxiety serves as a predictor of the sensory dimension of the pain experience, whereas depression serves as
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.388 | 0.249 |
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".