Bibliographic record
Abstract
When neurologists get together the conversation often drifts to unusual cases. But individual cases are seldom written up any longer, although there are reasons to do so. One, says Monk,1 is to report the “curiosities which we will never encounter in our every day practice, but which are so remarkable that they make a delightful interlude in our heavier reading.” Such is the case of the farmer with neuropathic pain and his therapeutic use of a cow fence. In 1978 a neurosurgical colleague asked if I would see a man who had longstanding chronic pain in his leg. He thought the man would interest me as at the time I was carrying out a project on phantom pain, studying whether the pain and the phantom after amputation would change with varying forms of electrical stimulation. He explained that the man didn’t have an amputation but he did use an interesting form of electrical therapy for leg pain. The man was a very pleasant 69-year-old farmer who had been wounded in his right lower leg and ankle by a mortar shell when serving as a Canadian infantryman in Italy in 1944. He lost a lot of blood from his open wounds and was transported on an open flatbed truck 45 miles behind the lines to a field hospital where the wounds were closed and the leg put in a …
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".