Bibliographic record
Abstract
To achieve this goal the institute divided its operation into four strands: two of the strands were the research areas - the study of advanced radiation therapy and biology, which worked separatively but cooperatively; a third was patient care; and the fourth element was leadership, provided by the clinical chiefs, the heads of the research divisions, and the administration, in particular the institute's first administrator, John Law. Together these strands helped create a philosophy that made the Ontario Cancer Institute unique and provided the basis for its national and international success. Essential to these successes was a new graduate department, Medical Biophysics, based in the University of Toronto School of Graduate Studies. This department, which provided an innovative, research-based doctoral and masters program, meant that the OCI could accurately be described as a centre for cancer treatment, research, and education. McCulloch describes how the first quantitative assay for stem cells played a major role in bringing OCI research to the international stage as well as influencing other science and much of the clinical thinking in the Institute. Other major advances that brought international recognition have been the identification of the mechanisms that allow cancer cells to resist death from the effects of a variety of different tumours and the isolation of the gene that encodes the T cell receptor, a critical part of the immune apparatus for dealing with foreign cells and viruses. McCulloch also details how lack of space to meet growing demands was a continuing source of frustration and disagreement, and how sometimes serious interpersonal problems hindered the forward thrust of development. Describing these events as well as institute's successes, he provides an insight into the history of Canada's premier cancer research centre.
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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.404 | 0.181 |
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".