Data Comparability Between Biomonitoring Studies for PCDD/Fs—Issues for the Use of the National Health and Nutrition Examination Survey (NHANES) Data
Bibliographic record
Abstract
O-29A5-4 Background/Aims: The goal of this study was to examine data from other case studies that used National Health and Nutrition Examination Survey (NHANES) data to compare individuals or populations for blood levels of polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/Fs). Our aim was to evaluate whether the data collected from a number of studies could be correctly compared to NHANES data. Methods: The methods used in the collection and analysis of samples for both NHANES and other case studies were examined to determine if methodologies were similar enough to conduct direct comparisons of blood PCDD/F data. Results: Numerous considerations and issues were discovered when examining other studies and their comparison to NHANES data. These included detection limits from their results being higher than those generated by Centers for Disease Control and Prevention (CDC). These artificially amplify the calculated toxic equivalents (TEQs) for individuals. NHANES uses enzymatic lipid determinations to calculate blood lipids and many studies still use gravimetric lipid determinations. This further amplifies perceived TEQs for individuals being compared to NHANES. These errors combined with other data quality issues are exacerbating exposure scenarios and potentially causing a misclassification of individuals or study cohorts. Conclusion: Many studies that are comparing their data to NHANES data are doing so incorrectly. They are either not conducting the appropriate statistical treatment of the NHANES data or they have results from laboratories that are not capable of producing the quality of data required to compare with the NHANES dataset and therefore, misrepresenting the exposures that they are reporting in their studies.
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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 | 0.007 | 0.028 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".