Bibliographic record
Abstract
Mark Vincent, M.B., Ch.B., a fellow of the Royal College of Physicians, Canada, is an oncologist at the London Regional Cancer Center, Ontario. There he is running a trial of the Cuban vaccine in advanced non-small cell lung cancer patients who haven’t been helped by chemotherapy. It is the vaccine’s first trial outside its homeland and Vincent would clearly like that soft-pedaled. In talking to a reporter, he made a point of telling her that he would not “want to be associated with any particular political viewpoint.” The conversation then turned to the rationale for the vaccine, starting with Vincent’s reminder that circulating estrogen—the estrogen that floats in the blood as a matter of course—can drive breast cancer growth. Similarly, he said, certain other cancers—non-small cell lung and many head and neck cancers, for instance—can be fueled by epidermal growth factor (EGF), which also circulates in the blood. The vaccine, accordingly, is meant to cause a patient’s immune system to form antibodies to EGF that will prevent its doing that. In a pilot study in Cuba, all patients given the vaccine have had advanced non-small cell lung cancer. The study is ongoing. In a follow-up study, the 80 patients in the Canadian trial, which opened last September, are being randomized to get or not get the vaccine. Vincent is not sure of definitive results. “We may find,” he said, “that our end-stage people with non-small cell lung cancer do not mount an immune response to anything because they are too immuno-depressed. If so, we may need to move to a randomized study of the vaccine in patients with slightly less advanced disease.” In the current trial, however, detecting survival differences between the treated patients and the controls is secondary to assessing the vaccine’s safety and immunogenicity. It is not, Vincent indicated, that the Cuban findings on these scores are invalid; they are not. In fact, after a trip to Havana with a Canadian Medical Research Council group, he came away thinking that many of the scientists he met there “seemed quite good and could probably find employment anywhere in the western world.” The difficulty, he said, was that “our [Canadian] patients are somewhat different from those in Cuba in that they have had chemotherapy of a different sort and may be at a different point in their natural history [of non-small cell lung cancer].” Besides, he added, the randomized design of the Canadian trial allows you to learn to what degree the vaccine is immunogenic. Otherwise, you might never know because it so happens that “there is a natural antibody level to EGF in everyone and it may fluctuate.” For all that, Vincent thinks the vaccine was soundly conceived. “It deserves a proper scientific test,” he said. “That’s where I am coming from.” Dr. Mark Vincent
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".