Bibliographic record
Abstract
Manner of death determination is a basic and traditional task for medical examiners. Though it has a history spanning centuries, many facets of manner determination continue to be problematic. Few issues are more vexing than the difficulties in determining intent. A survey of members of the National Association of Medical Examiners was performed to determine current practice and attitudes about the use of intent in manner determination. There were 168 completed responses, representing an approximately 20% response rate. The results reveal significant variation. While the concept of “volition” as distinct from “intent” has been common for over a decade, less than 60% of respondents made the distinction in their practice. There was also wide variation in the degree of certainty practitioners noted they used when determining manner with peaks at between 51-60% and 95-99%. Some of this represents real uncertainty in manner determination and some represents cultural and regional differences in practice. The variation and uncertainty in these determinations has led some to suggest that this role be abandoned. However, the statistical, policy, and cultural need for these determinations dictate that the function will be performed even if abandoned by the medical examiner community. Medical examiners are uniquely positioned and trained to fill this need, and should continue to do so.
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 | 0.014 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".