A bibliometric analysis of scientific production in cancer molecular epidemiology
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
OBJECTIVES: The main purpose of this research was to compare the scientific production in the field of cancer molecular epidemiology among countries and to evaluate the publication trend between 1995 and 2004. METHODS: A bibliometric study was carried out searching the PubMed database with a combined search strategy based on the keywords listed in the medical subject headings and a free text search. Only articles from a representative subset of 92 journals--accounting for 80% of papers identified--were selected for the analysis, and the resulting 13,240 abstracts were manually checked according to a list of basic inclusion criteria. The study evaluated the number of publications and the impact factor (mean and sum), absolute and normalized by country population and gross domestic product. RESULTS: A total of 3,842 citations were finally selected for the analysis. Thirty-seven percent came from the European Union (UK, Germany, Italy, France and Sweden ranking at the top), 31.6% from USA and 9.7% from Japan. The highest mean impact factor was reported for Canada (6.3), USA (5.9), Finland (5.8) and UK (5.2). Finland, Sweden and Israel had the best ratio between scientific production and available resources. 'Genetic polymorphism, glutathione transferase, breast neoplasm, risk factors, case-control studies and polymerase chain reaction' were the most used keywords in each of the subgroups evaluated, although inclusion criteria may have privileged studies dealing with exogenous carcinogens. CONCLUSION: Cancer molecular epidemiology is an expanding area attracting an increasing interest. The identification of an operative definition is a necessary condition to give to this discipline a unique scientific identity.
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 | 0.087 | 0.089 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.919 | 0.981 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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