What is the most appropriate treatment for patellar tendinopathy?
Bibliographic record
Abstract
LeadersThe medicolegal aspects of automatism in mild head injury Automatism or automatic behaviour was originally described in the Hippocratic corpus in relation to sleepwalking and other nocturnal behaviours. 1 Despite its long history, this area of automatism remains confused and imprecise in the medical and legal literature.Within English common law, it is a fundamental principle that the intent (mens rea) and the act (actus reus) must occur together to constitute the crime. 2As such, the absence of a mens rea means that the person at that point in time lacks the intent to commit a crime.In the legal view, post-traumatic automatism is a form of "sane" automatism because it results from an external factor, for example, a blow to the head, rather than from a disease of the mind (which is responsible for "insane" automatisms). 3 4As a legal defence under English law, if successful, post-traumatic automatism leads to acquittal rather than the judge deciding the disposal as in the case of insane automatism. 5 In recent years, a number of cases of footballers appearing before disciplinary tribunals for striking and other charges have claimed in their defence that they suVered a prior concussive injury and at the time of the alleged incident were suVering from a "post-traumatic automatism" and as a result were not responsible for their actions.In one celebrated case in Australian football, this defence was successful and resulted in the sport's administrative body developing specific guidelines to outlaw this potential defence.This topic of post-traumatic automatism has only a limited amount of published information to guide practitioners, players, administrators, and lawyers and this paper seeks to establish appropriate medical guidelines in this area.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".