Self-directed reaching and grasping rehabilitation using automatic pellet presentation system after cervical dorsolateral quadrant injury in female rats
Bibliographic record
Abstract
STUDY PURPOSE: This data was collected as part of a study on the advantages of self-directed rehabilitation reaching and grasping training by using an automatic pellet presentation (APP) system. The goal of the study was to understand the factors that determine better rehabilitation-induced recovery in forelimb motor function after cervical spinal cord injury in rats exposed to the APP training system. DATA COLLECTED: The dataset contains data from 19 unique female Lewis rats with a unilateral dorsolateral quadrant section injury at cervical level C4. Animals were exposed to the APP system up to 2 months before injury and up to 2 months after rehabilitation onset one week after injury. Metrics in the data include the number of trials the animal performs in the APP system (number of attempts to reach a pellet) per day, the number of trials that are failed per day, the number of trials the animal reached but dropped per day, and the number of trials that the animals successfully reaches and grasps per day. Summary metrics includes cumulated values per day and averages at baseline (last 2 weeks of training before injury), rehabilitation onset (3 first days of rehabilitation) and rehabilitation final (last week of rehabilitation period). Summary measures of injury are included. Principal component scores for the first 3 components resulting from a non-linear principal component analysis on the summary metrics are also included, as well as the results of a k-mean clustering analysis. DATA USAGE NOTES: The analysis of this data shown in the originating publication indicates that both intensity and amount of training is one of the major factors influencing recovery induced by performing the single pellet task with the APP system. However, the analysis also indicate that rehabilitation training becomes progressively less efficient as both the amount and intensity of rehabilitation training increases.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".