Iodine nutritional status in Brazil: a meta-analysis of all studies performed in the country pinpoints to an insufficient evaluation and heterogeneity
Bibliographic record
Abstract
Objectives Iodine deficiency disorder (IDD) is the result of an inadequate dietary intake of iodine, which physiological consequences are endemic goiter and thyroid dysfunction. The objective of this study was to a analyze studies that assessed the status of Brazil’s population iodine nutrition and IDD prevalence. Materials and methods Systematic review using PRISMA statement. Electronic database: PubMed, Medline, SciELO and Lilacs. Quality of studies: Newcastle-Ottawa Scale. Meta-analysis was carried out with R Core Team Statistical Software, version 3.1.0 (2014). The summary measure (WMD) and its confidence interval (CI) of 95% were calculated. The “Funnel plot” graph assessed publication bias and heterogeneity. Results Seventeen papers were eligible: pregnant women (2), school children (9), adults/elderly (4) and preschool children/infants (2). Geographic distribution: North (1), Northeast (1), Midwest (2), Southeast (13), South (3). Twenty-three thousand two hundred seventy-two subjects were evaluated between 1997 and 2013 and all have use urinary iodine (UI) measurement. However, only 7 studies could be included in meta-analysis, all from Southeast region. The overall prevalence of IDD in school children in southeast region was 15.3% (95% CI, 13-35%), however this data had an important heterogeneity, expressed by the I2 Statistic of 99.5%. Conclusion Only few studies have been performed and enrolled populations from south/southeast region of Brazil. The actual IDD prevalence analysis is complex because it was detected bias due influence of individual studies and very high heterogeneity. IDD might still be high in some areas but this remained unknown even after this meta-analysis evaluation. The generation of a national program for analysis of iodine status in all regions is urgently required. Arch Endocrinol Metab. 2015;59(1):13-22
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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 | 0.029 | 0.054 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.043 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".