Diagnostic Accuracy of Two Food Insecurity Screeners Recommended for Use in Health Care Settings
Bibliographic record
Abstract
OBJECTIVES: To test the diagnostic accuracy of the American Academy of Pediatrics (AAP) recommended food insecurity screener. METHODS: We conducted prospective diagnostic accuracy studies between July and November 2016 in Chicago, Illinois. We recruited convenience samples of adults from adult and pediatric emergency departments (12-month recall study: n = 188; 30-day recall study: n = 154). A self-administered survey included the 6-item Household Food Security Screen (gold standard), the validated 2-item Hunger Vital Sign (HVS; often, sometimes, never response categories), and the 2-item AAP tool (yes-or-no response categories). RESULTS: Food insecurity was prevalent (12-month recall group: 46%; 30-day group: 39%). Sensitivity of the AAP tool using 12-month and 30-day recall was, respectively, 76% (95% confidence interval [CI] = 65%, 85%) and 72% (95% CI = 57%, 84%). The HVS sensitivity was significantly higher than the AAP tool (12-month: 94% [95% CI = 86%, 98%; P = .002]; 30-day: 92% [95% CI = 79%, 98%; P = .02]). CONCLUSIONS: The AAP tool missed nearly a quarter of food-insecure adults screened in the hospital; the HVS screening tool was more sensitive. Public health implications. Health care systems adopting food insecurity screening should optimize ease of administration and sensitivity of the screening tool.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".