Bibliographic record
Abstract
Exposure-response assessment methods have shifted towards more quantitative approaches, with health risk assessors exploring more statistically driven techniques. These assessments, however, usually rely on one critical health effect from a single key study. Categorical regression addresses this limitation by incorporating data from all relevant studies – including human, animal, and mechanistic studies – thereby including a broad spectrum of health endpoints and exposure levels for exposure-response analysis in an objective manner. Categorical regression requires the establishment of ordered response categories corresponding to increasingly severe adverse health outcomes and the availability of a comprehensive database that summarizes all data on different outcomes from different studies, including the exposure or dose at which these out-comes are observed and their severity. It has found application in the risk assessment of essential nutrients and trace metals. Since adverse effects may arise from either deficient or excess exposure, the exposure-response curve is U-shaped, which provides a basis for determining optimal intake levels that minimize the joint risks of deficiency and excess. This article provides an overview of the use of categorical regression fit exposure-response models incorporating data from multiple evidence streams. An extension of categorical regression that permits the simultaneous analysis of excess and deficiency toxicity data is presented and applied to comprehensive databases on copper and manganese. Future applications of cat-egorical regression will be able to make greater use of diverse data sets developed using new approach methodologies, which can be expected to provide valuable information on toxic responses of varying severity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.076 | 0.160 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.020 | 0.018 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".