“How do you live without a stomach?”: A multiple case study examination of total gastrectomy for palliation or prophylaxis
Bibliographic record
Abstract
OBJECTIVE: The number of diagnosed cases of stomach cancer in Western countries is relatively small compared to prevalence rates in Eastern populations. This disparity creates a general lack of information and understanding of the experience of patients treated for this disease in North America. Surgical removal of the stomach, also called total gastrectomy (TG), is presently the only curative treatment available to patients with stomach cancer. Considering the impact such a procedure may have, very little is known about what factors influence an individual's postsurgical quality of life (QL). METHOD: This article reviews current literature and examines three unique case studies. Semi-structured interviews were analyzed using content analysis, a qualitative analytic approach for reporting combined subject responses. RESULTS: Participants included one 37-year-old man with multiple polyps in his stomach and a family history of stomach cancer, one 18 year-old man with a confirmed CDH1 mutation and a family history of stomach cancer, and one 33-year-old man with confirmed metastatic gastric adenocarcinoma. Subjective patient experience was categorized into: (1) making the decision, (2) treatment impact, and (3) life after TG. Prior to surgery, all patients carefully evaluated their perceived risk compared to the treatment consequences and indicated that a certain event triggered their decision. The largest treatment impacts were learning to eat again and adjusting to the physical changes. Each patient endorsed that their experience made them appreciate and make the most of life. SIGNIFICANCE OF RESULTS: This currently represents the only study to investigate the lived experience of TG for prophylaxis or palliation in individuals with and without genetic risk for stomach cancer. Understanding this process will allow all members of the cancer care team, and the patients themselves, to better understand the factors involved in decision making and postoperative adjustment. Fruitful avenues for future research are discussed.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".