Locomotor Recovery in Spinal Cord Injury Can Be Assessed Using Weight-Bearing
Bibliographic record
Abstract
Spinal cord injury (SCI) can result in partial or full paralysis, depending on the level and completeness of injury. Locomotor function is often used as a measure of recovery and treatment outcomes. The Basso, Beattie, and Bresnahan scale and Basso Mouse Scale (BMS) are gold standards used in rodent SCI studies to evaluate changes in locomotor recovery. However, these scoring systems are observer-dependent measures that may be affected by the presence of an experimenter, particularly in studies where blinding is difficult. Observer-independent methods measure outcomes without an operator present, thus reducing bias and increasing reproducibility between research groups. Changes in locomotor recovery were evaluated after contusive SCI using the Advanced Dynamic Weight Bearing (ADWB) system, previously used successfully to assess acute and chronic pain. We observed a shift in body weight early after injury, with increased surface area and weight placement to the front paws and the trunk/tail region. Concurrently, there was a reduction in rear paw surface area and weight placement. As functional recovery occurred over time, there was a shift toward reduced weight placement on the front paws. As with locomotor recovery, these changes did not return to preinjury levels. We also found that the rate and degree to which mice shifted weight onto front paws depended on injury severity. Importantly, changes in weight distribution and surface area showed a strong correlation with BMS scores, suggesting that the observer-independent ADWB test is a viable measure to assess changes in locomotor function over time after SCI.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".