The quality of life trajectory of resected gastric cancer
Bibliographic record
Abstract
BACKGROUND: Few studies describe quality of life (QoL) outcomes following gastrectomy for gastric cancer using a validated instrument. The gastric cancer module for the Functional Assessment of Cancer Therapy system of QoL measurement tools (FACT-Ga) was utilized to determine the changes in QoL following gastrectomy, and during the disease course. METHODS: In 43 patients undergoing gastrectomy for gastric cancer, outcome such as complications, recurrence, and survival were annotated. Karnofsky performance status (KPS) and QoL were determined preoperatively and at each follow-up visit. RESULTS: Nineteen (44%) patients and 24 (56%) patients underwent partial gastrectomy (PG) and total gastrectomy (TG), respectively. Complications occurred in 30%, and one mortality (2.3%) occurred. Median survival was 23 months. KPS, FACT-G, and FACT-Ga scores all decreased after surgery, and normalized by 6 months. There was no significant difference in QoL in patients who had a PG or TG, although the type of gastrectomy did affect KPS. QoL dropped on average 4.4 ± 3.6 months prior to death. CONCLUSIONS: Surgery adversely affects QoL for up to 6 months. Thereafter, QoL mirrors changes in disease status. More studies are required to document the QoL cost-benefit ratio in gastric cancer, which often is accompanied by short survival benefits.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".