Bibliographic record
Abstract
Recent court decisions in the United States, Canada, and the United Kingdom have demonstrated confusion and uncertainty by the triers of fact, whether they be judges or juries, about the evidence presented in cases of alleged abusive head trauma. It is not surprising that judges and juries have a difficult time understanding and evaluating the evidence presented by the opposing sides of these courtroom arguments. Complicated scientific discourse by “dueling experts” who offer diametrically opposite views of the medical issues at hand cloud the issues even more. Efforts to define the admissibility of expert testimony by the courts have taken place. One such effort is the Daubert hearing,1 wherein the court attempts to determine if a theory that is being presented is generally accepted, whether it can be tested, and whether the theory depends on peer-reviewed publications, among other things. However, the Daubert hearing allows for wide interpretation by the courts, and it has limited usefulness in excluding those who would give irresponsible medical testimony. One of the medicolegal arguments in cases of abusive head trauma has been whether shaking an infant, in the absence of an impact, creates sufficient forces to produce all the classic injuries associated with abusive head trauma. … Address correspondence to Robert M. Reece, MD, Box 351, 800 Washington St, Boston, MA 02111. E-mail: rmreece{at}gmail.com
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.008 | 0.005 |
| 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".