The effects of burial on drug detection in skeletal tissues
Bibliographic record
Abstract
Skeletal tissues have recently been investigated for use in post-mortem toxicology. Variables affecting drug concentration in these tissues, however, are still poorly characterized. In this work, the relative effects of burial on the response of enzyme-linked immunosorbent assay (ELISA) and gas chromatography-mass spectrometry (GC-MS) assays were examined. Rats were acutely exposed to ketamine or diazepam, euthanized and buried outdoors. After one month, the remains were exhumed and skeletal tissue drug levels were compared those of non-buried rats. A climate-controlled burial was also undertaken using defleshed bones to approximate an extended decomposition. Long bones (femora, tibiae) were isolated and separated into tissue type (diaphyseal bone, epiphyseal bone, and marrow), and according to treatment (i.e. buried or non-buried). Following methanolic extraction (bone) or simple homogenization (marrow), samples were analyzed with ELISA. Samples were then pooled according to treatment, extracted by solid phase extraction (SPE) and confirmed with GC-MS. Under the conditions examined, the effects of burial appear to be drug and tissue dependent. Ketamine-exposed tissues demonstrated the greatest differences, especially in bone marrow. In diazepam-exposed tissues, burial did not seem to greatly affect drug response and some gave greater assay response compared to the non-buried set. Overall, the data suggest that fresh tissue samples may not be representative of decomposed samples in terms of skeletal tissue drug levels.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".