Can increasing whole and fractioned pea flour consumption in Canada reduce healthcare expenditures?
Bibliographic record
Abstract
The implication of increasing consumption of functional foods, such as pulse-containing products, reveals the potential to reduce the incidence of type 2 diabetes (T2D) and coronary heart disease (CHD) and thereby achieves the cost savings associated with treatment and productivity loss. This research investigates the economic impact of such an important aspect of dietary pulse intake. The objective of the research is to determine the potential annual healthcare savings resulting from pulse flour consumption at Health Canada’s recommended daily rates. This study employs a four-step cost-of-illness approach to estimate such savings: 1) estimation of success rate of the healthy food; 2) determination of lower glycemic index, insulin concentration reduction, and lower cholesterol; 3) assumption of reduction in prevalence of T2D and CHD; 4) calculation of cost savings with regard to reduced occurrence of T2D and CHD. The findings demonstrate that annual cost savings ranging from $ 43.8 to 317.8 million (T2D category) and $ 154.9 to 958.0 million (CHD category) can be achieved for the Canada’s health budgetary framework with the increased consumption of dietary pulses. The estimations of cost savings are contingent on four scenarios: ideal, optimistic, pessimistic, and very pessimistic. People susceptible to higher blood glucose, higher insulin, and higher total cholesterol could benefit considerably by substituting pulse-containing foods for unhealthy foods. The adaptation to a dietary pattern that includes pulses will result in significant expenditure reductions in Canada’s publicly funded health care system, lessening the economic burden of illness in Canada.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".