Bibliographic record
Abstract
Historically, our knowledge of peripheral nerves and peripheral nerve injuries (PNIs) came mainly from experiences on the battlefield. Sir Herbert Seddon published his PNI classification system while caring for the injured during the second world war (1942). Nevertheless, in modern times, it is not uncommon to encounter PNI in non-combat-related trauma cases. These injuries can be life-changing and are often associated with significant morbidity, potentially leading to significant disabilities. Given that they mostly present in young adults of working age, these disabilities carry lifelong implications for the patients.Peripheral nerve trunks are composed of three separate layers surrounding nerve fibers. The innermost collagenous endoneurium layer envelops the axonal fibers (myelinated or unmyelinated) to provide mechanical and metabolic support. Together they make up the nerve fascicles, each of which is surrounded by a flattened cellular layer called the perineurium. The outer most collagenous layer, called the epineurium, surrounds the fascicles. Knowledge of this anatomy is essential for comprehending the classifications, clinical findings, and prognosis of PNIs and, thus, the best possible management for each patient.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.033 | 0.010 |
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".