Bibliographic record
Abstract
The epidemics of obesity, metabolic syndrome and type 2 diabetes have worsened over the past decades. During this time our preventive and therapeutic approach (the ‘vaccine’), consisting of a low-fat diet and exercise, has remained fundamentally unchanged. A case is made that these conditions are inter-related and may be caused by a single underlying factor related to the carbohydrate content of diet. The validity of the present approach is challenged when those most knowledgeable in its application succumb to diseases it is meant to prevent. Others argue against the status quo that a low-carbohydrate diet may be more beneficial. A strong belief in the present approach discouraged research into low-carbohydrate diets until recently. Several studies have now demonstrated their benefits and are refuting old claims that they cause harm. Aboriginal people suffer more acutely from the epidemics in question and their dietary history suggests that a sudden increase in carbohydrates is to blame. Recent studies and a case history demonstrate that carbohydrate consumption can drive appetite and over-eating while carbohydrate restriction leads to weight loss and improvement in the markers for metabolic syndrome and type 2 diabetes. The growing evidence in support of low-carbohydrate diets will encounter resistance from economic interests threatened by changes in consumption patterns. So what? In the face of mounting new evidence, our rigid adherence to a low-fat diet approach can no longer be justified. We must be prepared to offer more flexible dietary recommendations based on emerging evidence that carbohydrate restriction has a salutary effect on obesity, metabolic syndrome and type 2 diabetes.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".