Does GLP enhance the quality of toxicological evidence for regulatory decisions?: TABLE 1.
Bibliographic record
Abstract
There is debate over whether the requirements of GLP are appropriate standards for evaluating the quality of toxicological data used to formulate regulations. A group promoting the importance of non-monotonic dose responses for endocrine disruptors contend that scoring systems giving primacy to GLP are biased against non-GLP studies from the literature and are merely record-keeping exercises to prevent fraudulent reporting of data from non-published guideline toxicology studies. They argue that guideline studies often employ insensitive species and outdated methods, and ignore the perspectives of subject-matter experts in endocrine disruption, who should be the sole arbiters of data quality. We believe regulatory agencies should use both non-GLP and GLP studies, that GLP requirements assure fundamental tenets of study integrity not typically addressed by journal peer-review, and that use of standardized test guidelines and GLP promotes consistency, reliability, comparability, and harmonization of various types of studies used by regulatory agencies worldwide. This debate suffers two impediments to progress: a conflation of different phases of study interpretation and levels of data validity, and a misleading characterization of many essential components of GLP and regulatory toxicology. Herein we provide clarifications critical for removing those impediments.
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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 | 0.157 | 0.339 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".