A magyar daganatos betegek túlélési esélye a Nemzeti Rákregiszter adatai alapján
Bibliographic record
Abstract
The Hungarian National Cancer Registry (HNCR) was launched in August, 1999 by the National Cancer Institute. The main goal of HNCR is to determine the prevalence of different types of malignant cancers. A new method, period analysis was invented to determine survival chances of patients with malignant tumor. Based on period analysis we developed a new method by approximating survivals of Hungarian cancer patients with the help of Gompertz distribution. Our survival analysis was based on HNCR data of patients with cancer recognized between January 1, 2002 and December 31, 2005. These data are far enough from the time when HNCR started, thus they do not contain the initial errors, but also far enough from the present so their correction could be considered completed. In case of 21 malignant tumor locations for males and 23 ones for females we determined the parameters of the Gompertz distribution and based on the estimated parameters we estimated the expected survival probabilities for each specific tumor type and gender. In this study we have not used the TNM-based clinical stage or any other data of the patients contained by HNCR. Using the Gompertz model, the complete recovery of a cancer patient is always possible and the probability of recovery has a reliable estimate based on a short follow-up period only. We compared our results with five-year survival data of Canada, Italy, Norway and Finland and we did not find substantial differences. For both men and women, considering any specific location, the differences in survival among countries are much smaller than the difference between locations.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.008 |
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".