Bibliographic record
Abstract
A Forensic Review: 30 Years Later I n July 1966, I began a study of consecutive forensic cases that ended approximately 6 years later, when exactly 100 cases had been seen.This practice review was published in the Journal in August 1973.It is instructive now to note the changes that have taken place since then.First, almost 30 years ago the editorial staffof the Journal were willing to publish a practice review: although today this is a standard audit procedure, I would suggest that they were ahead of their time!Next, it is clear that the types of cases have changed drastically.When I conducted my review, impaired driving and substance abuse accounted for a mere 6% of cases, there was only a single case of watch and beset (harassment), and there were no cases from the Workers' Compensation Board.Changes in divorce and auto accident insurance laws have since eliminated many of the grounds for seeking medical reports.Conversely, allegations of abuse, employment harassment, and the psychiatric sequelae of other accidents now require assessment.Thirty years ago, a lawyer would refer a client to an individual psychiatrist for a medicolegal examination and duly receive a typed report ofseveral pages.Today, with the use ofphotocopiers and electronic data storage and the existence ofmultifaceted forensic clinics, it is common to request the patient's entire medical file.This, when combined with clinic reports, has made legal procedures more rigid, drawn-out, and probably more costly.The most notable change, though, is society's loss ofrespect for the doctor's expertise and professional integrity.In 1966, the best legal advice for witness physicians was that the presence of a lawyer during an inquest suggested they might have something to hide.Today, this sounds rather quaint.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.010 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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".