Third Annual World Congress on the Insulin Resistance Syndrome
Bibliographic record
Abstract
This is the third of three articles reviewing presentations at the 3rd Annual World Congress on the Insulin Resistance Syndrome, San Francisco, California, 17–19 November 2005. Pamela Goodwin (Toronto, Canada) introduced the topic of insulin resistance and malignancy, pointing out that there is variation in cancer incidence around the world, with variations in diet and obesity as important determining factors, and perhaps with the insulin resistance syndrome (IRS) as the underlying state leading to both increased cancer risk and worse outcomes of cancers of breast, prostate, colon, and many other tissues. Mediators may include activation of tyrosine kinase signaling pathways of premalignant and malignant cells via the insulin, insulin-like growth factor (IGF)-1, and IGF-2 receptors. Insulin receptors are present on normal breast, colorectal, and other cells, and in cancer cell lines, binding of the insulin receptor activates the mitogen-activated protein kinase pathways, and insulin stimulates cell-cycle progression, with the potential to increase epithelial cell proliferation in colon and other tissues. Hormone receptor negative breast cancers, particularly those not expressing the progesterone receptor, may have increased signaling through the insulin receptor. There are two forms of the insulin receptor. The A form is mainly seen in the fetal state, and also in the adult central nervous system (CNS), with the B form seen in the adult. The A form has high-affinity IGF-2 binding, has mitogenic and antiapoptotic effects, and hybridizes with the IGF-1 receptor; therefore, a fruitful area of research in carcinogenesis may be the expression of this form of the insulin receptor. Gerald Reaven (Stanford, CA) noted that differential tissue insulin sensitivity may be important in the relationship between the IRS and malignancy. The dose-response curve of adipose tissue to insulin shows a greater degree of insulin effect than that seen in skeletal muscle. Similarly, in the IRS not every …
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.075 | 0.052 |
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".