Bibliographic record
Abstract
In an emergency situation, such as a chemical, biological, radionuclide, nuclear or explosion (CBRNE) event, all patient populations are at increased risk of serious adverse events. Therapeutic product (TP) safety and efficacy depend on the disposition of the product through absorption, distribution, metabolism and excretion. The ability of a patient to benefit from or merely tolerate a TP can be modified by many factors, including but not limited to culture, diet, disease, environmental contaminants, genetic predisposition, stress and socioeconomic status and recent life experiences. Metabolism is considered to have the greatest effect on safety and efficacy, as chemicals not metabolised can accumulate to toxic levels. Inter-individual variances in most drug metabolism enzymes may range up to greater than 1000-fold. The fetus, neonates, infants, individuals with hormonal change, infection or prior exposure to licit or illicit products and the elderly are more susceptible to increased risk of serious adverse health effects. The critically ill are the most at risk. The at-risk populations for a serious adverse event are dependent then on the CBRNE event, their physical and cognitive states and the inter-individual intrinsic and extrinsic factors that affect TP disposition.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".