Prevalence of Solid Neoplasms Diagnosed Between the Years of 2011 to 2016 and Oncologically Treated at the University Hospital of Santa Maria
Bibliographic record
Abstract
BACKGROUND: Cancer is a public health problem, especially in developing countries. In order to establish effective measures for the cancer control, there is a need for quality information, thus enabling a better understanding of the disease and its determinants, formulation of causal hypotheses, evaluation of the technological advances applied to prevention and treatment as well as the effectiveness of health care. The objective of the study was to investigate the prevalence of solid neoplasms diagnosed between the years 2011 to 2016 and treated at the Oncology Department of the University Hospital of Santa Maria and the existing oncological context. METHODS: This is an observational cross-sectional study. The target population was comprised of adult patients (18 years of age or older) and elderly people (60 years of age or older) diagnosed with solid cancer by anatomico-pathological examination between 2011 and 2016, who started oncological treatment, according to high complexity procedure authorization (APAC)/Oncology. RESULTS: A total of 2,757 patients were selected, of which 1,493 patients were female (54.2%) and 1,264 male (45.8%). The mean age at the time of initiation of treatment was 59.94 years for both sexes, with the 95% confidence interval (59.44 - 60.44). The majority of patients were aged 61 - 70 years, totaling 747 patients, followed by 718 patients between 51 - 60 years. In all 31 primary sites identified the most prevalent one of neoplasms are breast, prostate, colorectal and lung; and most cases were stage IV (1,039 cases). A percentage of the number of cases of breast and esophageal cancer was higher than expected, and in contrast to a low percentage of hepatocarcinoma, kidney cancer and central nervous system tumors. The patients came from the entire area of the fourth Health Coordinating Area, where 100% of the municipalities referred to the institution, as well as other nine locations belonging to other coordinators. CONCLUSION: Most of the data obtained are consistent with the Brazilian reality, not identifying any peculiar characteristic of this region of the study worthy of note, except for the difference in the prevalence of some types of cancer, a fact that deserves further studies. There were also no discrepancies in a regional analysis. Along with this work, it was possible to demonstrate the cancer situation and the profile of oncology patients attended at a reference center for 41 municipalities, mainly in the central region of Rio Grande do Sul state, which may be useful in the elaboration of public policies to modify the profile identified, and serve as the basis for further studies in this region.
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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) | 0.002 | 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".