Bibliographic record
Abstract
muscular stiffness in young labrador retrievers A novel movement disorder characterised by extreme generalised stiffness has recently been recognised in related labrador retrievers in the UK (vanhaesebrouck and others 2011). This disease appears to be emerging, since new cases continue to be diagnosed. Most dogs have been presented to orthopaedists or described by their owners as ‘lame’, although the disease is primarily neurological. All dogs so far diagnosed have been male labrador retrievers. The muscular stiffness develops between two and 16 months of age. The stiffness is generalised, severe and persists during rest. It results in restricted joint movements (Fig 1). Affected dogs tend to shift their bodyweight forwards, and ultimately develop difficulties in standing up. Conscious electromyographic examination confirms the diagnosis. This test can be offered by most neurologists. In two affected dogs that died, we examined the brain and spinal cord and found a decrease in spinal interneuron counts together with a reduction in neurons in specific motor nuclei in the brain. Attempts to control the condition with muscle relaxants, anticonvulsants, immunosuppressants and other drugs have so far been unsuccessful, but nSAIDs seem to provide some help. The exact cause of this generalised muscular stiffness remains unknown. At the moment, pedigree analysis and genetic research on excess blood samples or cheek swabs are ongoing. We are therefore asking FIG 1: ‘Stiff ’ labrador retriever, in which extension of the hips was severely limited
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.251 | 0.122 |
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".