Bibliographic record
Abstract
AIM: To investigate the relevant prognostic factors and their differences between colorectal cancer (CRC) patients of Chinese Han and Hui ethnicities in the Beijing region. METHODS: A retrospective analysis of 880 patients diagnosed with CRC at Xuanwu Hospital, Capital Medical University between September 2001 and September 2011 was performed. Among the 880 patients, 398 and 482 were Hui and Han, respectively. Characteristics including sex, age, diet, tumor size, primary tumor site, Dukes' stage and degree of differentiation were analyzed for their influence on prognosis. Data on dietary structures were recorded through a questionnaire survey conducted during the patient's first visit, return visit or follow-up checkups. RESULTS: Among patients with colon cancer, the 5-year survival rate for patients of Hui ethnicity was lower than that for Han patients (P = 0.025). Six risk factors (age of onset, dietary structure, tumor size, Dukes' stage, location of cancer and degree of differentiation) in both Han and Hui patients were identified as prognostic factors (P < 0.05). Multivariate analysis showed that age of onset (P = 0.002), diet (P = 0.000), Dukes' stage (P = 0.000) and degree of differentiation (P = 0.000) are prognostic factors affecting both ethnic groups. Comparison of prognostic factors between Han and Hui patients with CRC showed that dietary structure was a statistically significant factor, and diet varied significantly between the two ethnic groups. CONCLUSION: Dietary structure has a significant influence on colon cancer prognosis among Han and Hui patients with colon cancer in Beijing, which may cause a difference in their survival rates.
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 | 0.000 | 0.001 |
| 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".