The Association of Physical Activity and Quality of Life in Gastric and Esophageal Cancers
Bibliographic record
Abstract
Patients with esophageal and gastric cancer typically present with an advanced tumour formation and severe symptoms such as progressive dysphagia, nausea, and weight loss (J. Lagergren & Lagergren, 2010; Zali, Rezaei-tavirani, & Azodi, 2011). Surgical treatment is the preference for both diagnoses, however often results in functional digestive complications (Bhargava & Chasen, 2010). Given the myriad of symptoms pre and post treatment, it is clear that health related quality of life (HRQL) is affected for these patients. PA has consistently and repeatedly improved HRQL in cancer patients (Ferrer, Huedo-Medina, Johnson, Ryan, & Pescatello, 2011). Only a few studies have identified potential PA effects in gastric and/or esophageal cancers, with inconsistent results (Bhargava & Chasen, 2010; Feeney, Reynolds, & Hussey, 2011; Lee et al., 2010; Na, Kim, Kim, Ha, & Yoon, 2000; Tatematsu, Park, Tanaka, Sakai, & Tsuboyama, 2013; Tatematsu, Ezoe, et al., 2013; Zalina, Lee, & Kandiah, 2012). To further explore the relationship between PA and HRQL in gastric and esophageal cancers, a cross-sectional one-time survey was completed in a GI outpatient clinic in Calgary, AB. Pearson correlations revealed a positive moderate association between total PA minutes and HRQL (r=.40, p=.03). Future research should consider interventions (education or programming) that promote PA participation in this unique population.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".