Correlation between Cancer Research Trends and the Importance of Cancers based on Mortality and Diagnosis Rates: An Analysis of Altmetric Data
Bibliographic record
Abstract
Aside from the infrequent news pertaining to medical breakthroughs or dangers to the public, medical research, especially in the field of cancer, is rarely discussed in depth. The public does not know the process in which specific fields of medical research receive funding, or how this funding is used to limit issues such as cancer. This study aims to provide clarity on cancer research trends. The amount of research papers pertaining to different types of cancers is compared against mortality and diagnosis rates to determine the amount of research attention given to a type of cancer, in relation to its effects on the general population. Computational tools, such as Python, R, and Microsoft Excel, were used to analyze a dataset of research papers. Python was used to parse through JSON files and extract the abstract and Altmetric score of cancer research papers. R was used to count the appearance of each type of cancer in the abstracts, and create histograms describing Altmetric scores and file frequency. Microsoft Excel was used to find correlations between Altmetrics’ data and Canadian Cancer Society data, linking the amount of research to the impacts of cancer based on deaths and new cases. The analysis from these tools revealed that breast cancer was the most researched cancer by a large margin, with nearly 1,700 papers, which is approximately four times the amount of the next leading type of cancer – prostate cancer. Although there were many research papers on the field of cancer, the Altmetric scores revealed that most of these papers did not gain significant online and media attention. Comparing these results to Canadian Cancer Society data showed that breast cancer was receiving more research attention than the mortality and diagnosis rates suggest it should. There were four times more breast cancer research papers than the secondmost researched cancer, prostate cancer. This was despite the fact that breast cancer was fourth in mortality and third in new cases among all types of cancer. Inversely, lung cancer was underrepresented, with only 401 research papers, despite being the deadliest cancer in Canada.
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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 | 0.016 | 0.154 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.040 | 0.070 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".