Lower iron stores were associated with suboptimal gross motor scores in infants at 3–7 months
Bibliographic record
Abstract
AIM: To investigate associations between iron status and gross motor scores in infants aged 3-7 months. METHODS: In a prospective study, 252 infants aged 3-7 months were examined using the age-standardised Alberta Infant Motor Scale (AIMS) prior to analysing iron status in 250 infants. Combined AIMS and ferritin results were assessed in 226 infants, whereas AIMS and reticulocyte haemoglobin (ret-Hb) results were obtained for 61 infants. We used logistic regressions and receiver operator characteristics to analyse our data. RESULTS: With AIMS z-score <10th percentile as outcome measure, optimal cut-off value for ferritin was 51 μg/L (sensitivity 86%, specificity 81%) and 28 pg for ret-Hb (sensitivity 86%, specificity 85%). The area under the curve for ferritin and ret-Hb was 0.886 and 0.896, respectively (n = 61). Ferritin <51 μg/L predicted an AIMS z-score <10th percentile in a logistic regression (OR 3.3, 95% CI 1.4-7.5, p = 0.006, n = 226). Six of 14 (43%) infants with ret-Hb <28 pg scored <10th percentile on AIMS compared to 1/47 (2.1%) infants with ret-Hb ≥28 μg/L (Exact, p < 0.001). CONCLUSION: Reticulocyte haemoglobin of <28 pg and ferritin <51 μg/L were associated with suboptimal gross motor scores in infants 3-7 months.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".