Poor Science; Poorly Trained Scientists; Poor Policies: Major Deterrents to the War on Cancer
Bibliographic record
Abstract
Although the availability of funding has been described as the major limitation on advances in cancer, the progress in the war on cancer has been deterred mainly by poor science, poorly trained scientists, and poor NIH policies. This is the result of NIH policies of its extreme focus on molecular biology (genomics, molecular genetics, molecular biology) identification of the molecular factors and pathways; which are required for the acceptability of treatment and preventive protocols. As such, this has influenced virtually all agencies that provide grants for medical research to adopt the NIH policies. This has impacted the funding of the research as well as the focus of the training of scientists. Directors of NCI Dr. Varmus (also Nobel Prize awardee) and Dr. Zerhouni had addressed this issue; and they rejected the necessity of molecular biology studies and information. NIH should return to the holistic physiological/pathophysiological approach to studies of cancer issues. This would provide the best approach for winning the war on cancer.
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 | 0.058 | 0.136 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.012 | 0.012 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.012 | 0.023 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".