Bibliographic record
Abstract
hat does cancer mean to you?It can affect the ones that we are closest to, the ones that we love, and for many of us it already has.We always hear about this disease -another risk factor, another therapy, another statistic.It is easy to get lost in this sea of information without fully appreciating the evolving knowledge on cancer.Today, the incidence of cancer is growing, but the rate of cancer mortality is decreasing with better treatment options*.This issue, in collaboration with the McMaster Cancer Society (MCS), strives to uncover the current advancements and concerns in the field of oncology.Specifically, the origin of cancer is discussed, with an investigation into the cancer stem cell hypothesis.Those interested in targeting cancer cells can explore the therapeutic potential of oncolytic viruses, nanovectors, and the Kanzius machine.Additionally, important issues surrounding the ethics of palliative surgery and other psychosocial implications of cancer are described.This issue provides an overview of cancer therapies and other implications during recovery.This special issue materialized through the joint effort of the McMaster Meducator and the McMaster Cancer Society.The goals of MCS are to support those impacted by cancer and bring awareness to the various issues surrounding this disease.While the society organizes several annual events, such as the Pink Ribbon Campaign and Cranes for Cancer, they continuously provide cancer information to others through a monthly newsletter and website (www.maccancersociety.com).Now, the Meducator provides an additional medium for which students and the community can learn more about the clinical issues and current research within the rapidly expanding field of oncology.Since this issue is completed funded by community donations, we would like to thank those who donated towards the printing and production of this special issue.Without your support and generosity, we could not educate our readers on the ethical concerns and promising research in cancer.Finally, please visit our website, www.meducator.org,
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.027 | 0.014 |
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; both teacher heads agree on what is shown here.
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".