Restaurant meals ‐ almost a full day's worth of calories, fats and sodium
Bibliographic record
Abstract
The objective was to systematically analyze the nutritional profile (calories, fat, saturated fat, trans fat, sodium and cholesterol) of meals from chain sit‐down/family style restaurants. Using the “U of T Restaurant Database”, the nutritional profile of 3507 meals from 19 restaurants was calculated and analyzed. This included all potential meal combinations from restaurants that provided nutrition information online and had 10 or more locations nationally. On average, breakfast, lunch and dinner meals contained: 1129 calories (56% of the average daily 2000 calorie recommendation), 151% of the amount of sodium an adult should consume in a single day (2263 mg), 89% of the recommended daily fat intake level (58 g), 83% of the recommended daily saturated and trans fat intake level (16 g saturated fat and 0.6 g trans fat) and 60% of the daily value for cholesterol (179 mg). More than 80% of meals exceeded the daily recommended intake level for sodium (1500mg). Only 1% of meals had less than 600 mg of sodium, the “healthy level” for meals, according to the FDA. Meals identified by the restaurants as being “healthy” contained on average 474 calories, 13 g of fat (20% DV), 3 g of saturated fat (17% DV) and 752 mg of sodium (50% AI). These data demonstrate that addressing the nutritional profile of restaurant meals should be a major public health priority. Grant Funding Source : MS: CIHR/PICDP Fellowship, Ontario Graduate Scholarship, ML: U of T McHenry Grant, Canadian Stroke Network
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".