Demographic, clinical, and biochemical predictors of pica in a large cohort of blood donors
Bibliographic record
Abstract
BACKGROUND: Pica is characterized as repeatedly eating or chewing a non-nutritious substance including, but not limited to ice, clay and dirt, starch, raw pasta, chalk, coal, paint, or paper. Pica symptoms can be intense and addiction-like and disrupt quality of life. It is strongly linked to iron deficiency. Since substantial iron loss occurs during blood donation, blood donors may be susceptible to development of pica behaviors. METHODS: We investigated demographic, clinical, hematological, and biochemical factors associated with pica using univariable and multivariable logistic regression analysis in a cohort of 11,418 racially diverse blood donors. Pica was defined by questionnaire responses as consuming at least 8 oz of ice daily and/or consumption of non-ice substances regardless of the amount and frequency. RESULTS: Pica was present in 2.2% of the donors. The sensitivity and specificity of pica in iron-deficient donors were 36% and 82%, respectively. Lower ferritin (p = .001), non-Asian race (p < .001), higher red cell distribution width (p < .001), younger age, and restless legs syndrome (p = .008) were independently associated with pica. Female sex is associated with iron deficiency but was not an independent predictor of pica suggesting that iron deficient males and females were equally susceptible to the development of pica behaviors. Donors with normal ferritin levels also reported pica, reinforcing the role of non-iron related factors in its presentation. CONCLUSIONS: We have identified demographic, clinical, and biochemical predictors of pica that help identify those most at risk for developing pica behaviors, and thereby assist in its clinical diagnosis and treatment.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".