Accuracy Investigation of Phthalate Metabolite Standards
Bibliographic record
Abstract
Phthalates are ubiquitous compounds whose metabolites are usually determined in urine for biomonitoring studies. Following suspect and unexplained results from our laboratory in an external quality-assessment scheme, we investigated the accuracy of all phthalate metabolite standards in our possession by comparing them with those of several suppliers. Our findings suggest that commercial phthalate metabolite certified solutions are not always accurate and that lot-to-lot discrepancies significantly affect the accuracy of the results obtained with several of these standards. These observations indicate that the reliability of the results obtained from different lots of standards is not equal, which reduces the possibility of intra-laboratory and inter-laboratory comparisons of results. However, agreements of accuracy have been observed for a majority of neat standards obtained from different suppliers, which indicates that a solution to this issue is available. Data accuracy of phthalate metabolites should be of concern for laboratories performing phthalate metabolite analysis because of the standards used. The results of our investigation are presented from the perspective that laboratories performing phthalate metabolite analysis can obtain accurate and comparable results in the future. Our findings will contribute to improving the quality of future phthalate metabolite analyses and will affect the interpretation of past results.
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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 | 0.017 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".