Personalized adapted locomotor training for an individual with sequelae of West Nile virus infection: a mixed-method case report
Bibliographic record
Abstract
Background West Nile virus (WNV) can have severe consequences, including encephalitis and paralysis. Purpose: To describe the benefits of intensive locomotor training (LT) for an individual with a previous WNV infection resulting in chronic paraplegia. Case Description: The patient, who became a wheelchair user following standard rehabilitation, began LT 3 years post infection. Her goals included standing and walking with an assistive device and transferring independently. The intervention consisted of bodyweight-supported treadmill training and overground training, which involved walking, balancing, strengthening, and transferring activities. Outcomes: Following 5 months of LT, the patient ambulated independently with a walker at a speed = 0.34m/s. She walked 110.1 metres in 6 minutes and increased her Berg Balance Scale score by 17 points. These improvements were either maintained or further increased 3 months post LT. The patient’s perspectives on LT were collected through a semi-structured interview. A conventional content analysis, which uses data to drive themes, revealed three themes: (1) recalibrating goals, (2) outcomes (i.e. physical and psychological benefits, such as a sense of accomplishment), and (3) challenges of LT and effective coping strategies. Conclusions: The patient demonstrated improved balance and walking abilities. Intensive LT was feasible and effective for this individual with chronic paraplegia due to WNV infection.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".