Dietary Nitrate: Ward et al. Respond
Bibliographic record
Abstract
Manganese as a Potential Confounder of Serum Prolactinde Burbure et al. (2006) elegantly demonstrated that dopaminergic markers in the serum, namely prolactin and homovanillic acid, are affected in children exposed to cadmium, lead, mercury, and arsenic.These findings, at low environmental exposure levels, reinforce the potential of these metals to perturb dopaminergic function and optimal development.In spite of the strengths of the article, de Burbure et al. (2006) overlooked an important potential confounder.Specifically, the authors should consider the possibility that manganese confounded their data; if so, the data set should be reexamined.A strong relationship between manganese exposure and serum prolactin levels has been raised in multiple studies.Although prolactin levels serve as a direct measurement of monoamines or their metabolites in peripheral tissues (e.g., blood platelets, plasma, urine), plasma prolactin is also an indirect indicator of dopaminergic functioning, a target for excessive exposure to manganese (Mutti and Smargiassi 1998;Smargiassi and Mutti 1999).A concordance between neurocognitive deficits and manganese exposure also exists, including a recent study in children exposed to water manganese concentrations exceeding 300 µg/L (Wasserman et al. 2006).A significant and positive correlation between blood manganese concentrations and prolactin levels in cord blood has also been established (Tasker et al. 2004).Other examples abound, although negative relationships between manganese and prolactin have also been reported (Roels et al. 1992).The potential that exposure to manganese contributed to or confounded the effects of the four metals on serum prolactin levels in the cohorts studied by de Burbue et al. (2006) should be considered.If samples are available for additional analysis, correlations between manganese exposure and prolactin would be beneficial and welcomed by various health forums as the debate on safe manganese exposure levels and sensitive health effect biomarkers continues.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.101 | 0.026 |
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".