Application and interpretation of immunophenotyping data in safety and risk assessment
Bibliographic record
Abstract
The use of immunophenotyping during immunotoxicity investigations was first popularized in the 1980 s and has since become more integrated into diagnostic and non-clinical assessments. The data provided from immunophenotyping can serve as an initial source of information to guide decisions for additional, more advanced, immunotoxicity testing as well as for human health safety and risk assessment of drugs and chemicals. However, comprehensive guidance describing applications of immunophenotyping data in immunotoxicity investigations is lacking, particularly among regulatory bodies. Therefore, a critical examination is needed for the appropriate interpretations and potential misinterpretations of such data during the assessment of drug safety and chemical risk. As such, the current uses and implications of immunophenotyping data in human health safety and risk assessments has been evaluated to provide additional context for the application of current methodologies and guidelines. In addition, case studies are presented to highlight the challenges of interpreting immunophenotyping results along with incorporating the findings into immunotoxicity investigations. Based on the analyses of current approaches and methodologies, a decision flow is presented for use of immunophenotyping data during risk informed decision making.
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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| 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".