Postmortem Metaxalone (Skelaxin<sup>®</sup>) Data from North Carolina
Bibliographic record
Abstract
The North Carolina Office of the Chief Medical Examiner Toxicology Laboratory identified 61 cases from 2002 to 2014 where metaxalone was detected during routine postmortem drug screening in support of a determination of cause and manner of death. Decedents were divided into groups based on the manner of death with the goal of studying metaxalone concentrations in overdose and non-overdose situations (natural, accident, suicide and undetermined). Subgroups were established for cases in which metaxalone contributed to the cause of death (attributed) and cases in which it did not (unattributed). Attributed cases were divided into those where metaxalone additively combined with other drugs and cases in which the drug was present in sufficient amounts to be the primary cause of death, regardless of other drugs present and the concentrations of those drugs. The mean metaxalone concentration for the additive deaths was 14.2 mg/L with a median value of 11 mg/L (n = 18) and a mean metaxalone concentration of 36.7 mg/L with a median value of 32 mg/L (n = 9) for primary deaths. For unattributed metaxalone concentrations, the mean was 3.4 mg/L with a median value of 2.9 mg/L (n = 31). Of the 61 cases, 34% fall at or below a therapeutic concentration of ≤4 mg/L. The selected case studies offer valuable information regarding postmortem interpretation.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".