POTENTIAL HEALTHCARE SAVINGS FROM PLANT STEROL ENRICHED FOODS IN CANADA
Bibliographic record
Abstract
Increased consumption of foods containing plant sterols has the potential to reduce the incidence of coronary heart disease (CHD) and thus reduce costs associated with treating that disease in a significant way. This paper reports the results of an investigation of the potential monetary benefits of allowing foods enriched with plant sterols to be marketed in Canada. The objective of this research was to estimate the annual savings that would accrue to Canada’s single-payer publicly funded health care system if plant sterols were approved for use. If foods containing plant sterols are consumed at a sufficient rate, a reduction in CHD should follow. This research employs a variation of traditional cost-of-illness analysis entailing four steps: (i) estimation of a “success rate” (proportion of persons who would consume plant sterols at the necessary rate); (ii) presumption of blood cholesterol reduction due to plant sterol consumption; (iii) assumption of reduction in CHD that follows from blood cholesterol reduction; (iv) calculation of cost savings associated with reduced incidence of CHD. Calculations were carried out for four scenarios: ideal, optimistic, pessimistic, and very pessimistic. It was estimated that between $38 million (very pessimistic scenario) and $2.45 billion (ideal scenario) could be saved annually by Canada’s health care system with plant sterol enriched food products being made available for sale.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".