Toward a Checklist for Exchange and Interpretation of Data from a Toxicology Study
Bibliographic record
Abstract
Data from toxicology and toxicogenomics studies are valuable, and can be combined for meta-analysis using public data repositories such as Chemical Effects in Biological Systems Knowledgebase, ArrayExpress, and Gene Expression Omnibus. In order to fully utilize the data for secondary analysis, it is necessary to have a description of the study and good annotation of the accompanying data. This study annotation permits sophisticated cross-study comparison and analysis, and allows data from comparable subjects to be identified and fully understood. The Minimal Information About a Microarray Experiment Standard was proposed to permit deposition and sharing of microarray data. We propose the first step toward an analogous standard for a toxicogenomics/toxicology study, by describing a checklist of information that best practices would suggest be included with the study data. When the information in this checklist is deposited together with the study data, the checklist information helps the public explore the study data in context of time, or identify data from similarly treated subjects, and also explore/identify potential sources of experimental variability. The proposed checklist summarizes useful information to include when sharing study data for publication, deposition into a database, or electronic exchange with collaborators. It is not a description of how to carry out an experiment, but a definition of how to describe an experiment. It is anticipated that once a toxicology checklist is accepted and put into use, then toxicology databases can be configured to require and output these fields, making it straightforward to annotate data for interpretation by others.
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 | 0.402 | 0.493 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.027 | 0.011 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.017 | 0.022 |
| Open science | 0.013 | 0.018 |
| Research integrity | 0.010 | 0.015 |
| Insufficient payload (model declined to judge) | 0.022 | 0.028 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".