Bibliographic record
Abstract
comparing his work on spasticity in humans to my studies of spasticity in the sacral spinal rat (Bennett et al. 2004).His comments highlight many of the confusions that have shrouded the study of spasticity and give me a chance to clear the air on this topic.The first confusion relates to the complexity of spasticity and the lack of consensus on which aspects of spasticity are most relevant.The spastic syndrome in humans (or what Dietz calls the "spastic movement disorder") is a complex collection of clinical conditions, including excess muscle tone (hypertonus), changes in muscle properties (like contractures), excess reflex activity (hyperreflexia), muscle oscillations (clonus), and massive uncontrolled motoneuron firing and contractions (spasms; Bennett et al. 2004;Dietz and Sinkjaer 2007;Kuhn and Macht 1948).In my laboratory, we mostly study spasms triggered by brief cutaneous stimulation and we have shown these to result from excess motoneuron excitability (Bennett et al. 2004; Li et al. 2004a).Dietz has spent a lot of time studying muscle properties (Dietz et al. 1986) and emphasizes their importance in his letter to the editor (Dietz 2007).From this perspective, he criticizes research that does not focus on muscle properties, suggesting that it is not relevant to the study of spasticity.This seems a bit odd because upward of 80% of spinal cord injured humans suffer from spasms caused by excess motoneuron activity (Maynard et al. 1990), and thus understanding spasms is important.
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 | 0.006 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.031 | 0.050 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".