Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
AIM: The aim of this study was to examine the prevalence of household food insecurity in individuals reporting migraine within a large population-based sample of Canadians. METHODS: The Canadian Community Health Survey (CCHS) uses a stratified cluster sample design to obtain information on Canadians ≥12 years of age. Data on household food insecurity were assessed for individuals who reported having migraine or not, providing a current point prevalence. This was assessed for stability in two CCHS datasets from four and eight years earlier. Factors associated with food insecurity among those reporting migraine were examined and a logistic regression model of food insecurity was developed. We also examined whether food insecurity was associated with other reported chronic health conditions. RESULTS: Of 48,645 eligible survey respondents, 4614 reported having migraine (weighted point prevalence 10.2%). Food insecurity was reported by 14.8% who reported migraine compared with 6.8% of those not reporting migraine, giving an odds ratio of 2.4 (95% confidence interval 2.0-2.8%). This risk estimate was stable over the previous eight years. The higher risk for food insecurity was not unique to migraine and was seen with some, but not all, chronic health conditions reported in the CCHS. CONCLUSIONS: Food insecurity is more frequent among individuals reporting migraine in Canada.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it