Bibliographic record
Abstract
AIM: To examine the survival patterns and determinants of primary liver cancer in a geographically defined Chinese population. METHODS: Primary liver cancer cases (n=13 685) diagnosed between 1981 and 2000 were identified by the Tianjin Cancer Registry. Age-adjusted and age-specific incidence rates were examined in both males and females. Proportional hazards (Cox) regression was utilized to explore the effects of time of diagnosis, sex, age, occupation, residence, and hospital of diagnosis on survival. RESULTS: Crude and age-adjusted incidence rates in the study period were: 27.4/100 000 and 26.3/100 000 in males; and 11.5/100 000 and 10.4/100 000 in females, respectively. Cox regression analyses indicated that there was a significant improvement in survival rates over time. Industrial workers and older people had relatively poor survival rates. The hospital in which the liver cancer was diagnosed was a statistically significant predictor of survival; patients diagnosed in city hospitals were more likely to have better survival than those diagnosed in community/district hospitals. CONCLUSION: Patients diagnosed in recent years appeared to have a better outcome than those diagnosed in early times. There were also significant survival disparities with respect to occupation and hospital of diagnosis, which suggest that socioeconomic status may play an important role in determining prognosis.
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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 | 0.000 | 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".