Spatial data analysis in cancer epidemiological study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Recently we planned to conduct a project which applies GIS technologies with region-level statistics to map the incidence and mortality of cervical cancer, as well as Pap smear test results in certain regions of New Brunswick, Canada. By integrating GIS with other analytical technologies such as data mining, spatial analysis and case-control study, we will demonstrate the disease spatial clusters and discover the etiologic hypotheses and significant disease risk factors. Based on our project objectives, the purpose of this literature review is to provide an extensive review and comparison study on existing methodologies used in detecting disease clusters under cancer epidemiological domain and to conclude feasible methodologies for our project. This paper is organized following a study path: (1) data acquisition - issues in cancer data collection; (2) methodologies in data mapping; (3) methodologies in data analysis. It should be noted that this literature review is mainly based on review papers in recent past on following domains: cancer data, disease mapping, statistical methods in spatial analysis, space-time clustering, spatial data mining, and cluster analysis software. The conclusion we made after this extensive review is that spatial data mining is a new, promising way to detect clusters.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 it