Bibliographic record
Abstract
Patients with lipodystrophy run a risk of early coronary disease that is several times greater than the risk in the general population, physicians attending the recent Canadian Cardiovascular Society Congress were told. Dr. Robert Hegele, from the Robarts Institute at the University of Western Ontario, observed that in familial partial lipodystrophy there is absence of fat in the buttocks and arms, with central obesity. He noted a common ancestry among afflicted Canadians and discovered that the causative gene on chromosome 1q21 encoded nuclear lamin. The results indicate that defects in the nuclear envelope can have metabolic and, ultimately, cardiovascular consequences. Hegele has also been studying why there is an extraordinary 40% incidence of type 2 diabetes among some First Nations adults in Northern Ontario, versus the overall Canadian incidence of 8%. It appears that this is caused by an abnormality at a single residue in the amino acid sequence of hepatic nuclear factor-1 a, which appears unique to this population. Hegele then linked the emergence of diabetes to radical alterations in diet and lifestyle. Although this genetic trait was unimportant 50 years ago, it has now emerged as a key determinant of diabetes risk and a likely explanation of the increase in diabetes and coronary disease within the Aboriginal people of Northern Ontario. Dr. Simon Pimstone, from the Department of Medical Genetics at the University of British Columbia, addressed the role of pharmacogenomics in the treatment of cardiovascular disease. He suggested that the extensive genetic variability seen among humans has influenced interindividual variation in response to drugs. He said this variation has modulated, at least in part, the fatal adverse drug reactions that in 1999 accounted for more than 100 000 deaths in the US and were estimated to be the fifth leading cause of in-hosital death. Perhaps even more important is the strong likelihood that genetic factors will modulate the susceptibility to the beneficial effects of medications. For instance, for a particular pharmacogenetic marker, 49% of the population were low responders, 35% high responders and 16% nonresponders. At present, the only way for a clinician to tell which category a patient falls into would be empirical: try the medication, wait for the response and then make adjustments to dose or try another medication. However, if a patient's response — high, low or nonresponsive — could be predicted by a simple genetic screen, there would be less need for such empirical treatment and monitoring. In this way, the most appropriate treatment could be determined much sooner. Thus, the mass-marketing approach for drugs indicated for common conditions, such as hypertension, is unlikely to be the way of the future. Instead, genetic typing has the potential to “microsegment” the market in a boutique fashion. As a result, this may rescue some failed drugs and lead to changes in indications or even expand indications and appropriate patient populations for others. — This article was written by Dr. Paul Armstrong, an Edmonton cardiologist. Physicians interested in submitting similar reports should contact John Hoey, 800 663-7336 x2118; ac.amc@jyeoh.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.248 | 0.148 |
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".