NEUROLOGICAL RECOVERY AND FUNCTIONAL OUTCOME OF COMPLETE TRAUMATIC SPINAL CORD INJURY PATIENTS: AN OBSERVATION FROM BANGLADESH
Bibliographic record
Abstract
Background: Neurological recovery and functional outcome is the burning issue for the spinal cord injury patients where neurological recovery depends on the improvement of motor score and ASIA impairment scale.Objective: It was aimed to explore the neurological recovery and functional outcome of complete traumatic spinal cord injury patients in a rehabilitation center of Bangladesh.Materials and Methods: Data were collected retrospectively from the record of 437 SCI patients from June 2014 to June 2017..We collect initial neurologic deficit, ASIA, SCIM and demographic data at admission and neurological deficit ASIA and SCIM after three months to compare of their neurological extent as well as functional outcome.Results: Majority of the participants had traumatic paraplegia (63.6%) and the principle cause was fall from height (51.3%).26 (5.9%) of the patients were shifted into B from AIS A, 38 (8.7%) were shifted into C, 28 (6.4%) were shifted into D.Among 437 participants three hundred and thirty-eight (77.3%) were wheelchair-dependent and forty-nine (11.2%) were walking at the time of discharge.Conclusion: Though neurological and functional recovery is rare for the complete SCI, but the study has shown us a hopeful aspiration.Further research on a larger scale studies is necessary to generalize the result.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".