Fast and efficient analyses of the post-mortem human blood and bone marrow using DI-SPME/LC-TOFMS method for forensic medicine purposes
Bibliographic record
Abstract
For the first time the method DI-SPME/LC-TOFMS was used and developed in order to determine the large antidepressant drugs in real forensic cases. The aim of the study was to optimize the new DI-SPME/LC-TOFMS method for the quantification of the large group of psychotropic drugs such as benzodiazepines, selective serotonin reuptake inhibitors, selective serotonin and noradrenaline reuptake inhibitors, tricyclic antidepressants and sleeping pills "Z". The volume of the sample, adsorption time, post-adsorption purification and desorption time were precisely optimized. The validation parameters such as limit of detection and quantification, linearity, precision during and between days and the matrix effect were determined. All obtained values are within the acceptable range for toxicological analyses. The usefulness of the method was confirmed by analyzing the post-mortem samples. Drug concentrations were determined in real samples with high precision, which gives perspectives for the DI-SPME/LC-TOFMS routine application in toxicological and forensic analyses in the future.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".