Do all patients with functional motor-incomplete (AIS-D) traumatic spinal cord injury need specialized inpatient functional rehabilitation? <i>A prospective observational cohort study proposing clinical criteria for home-based rehabilitation after acute care</i>
Bibliographic record
Abstract
Context/Objective Functional motor-incomplete AIS-D traumatic spinal cord injury (tSCI) represents an important growing population in neuro-traumatology. There is thus an important need for establishing strategies to optimize SCI rehabilitation resources. This study aims at proposing eligibility criteria to select individuals who could be discharged home (home-based rehabilitation) after acute care following an AIS-D tSCI and investigate its impact on the long-term functional status and quality of life (QOL), as compared to transfer to inpatient functional rehabilitation (IFR) resources.Design An observational prospective cohort study.Setting A single Level-1 specialized trauma center.Participants 213 individuals sustaining an AIS-D tSCI.Interventions Home-based rehabilitation based on clinical specific criteria to be assessed by the acute care team.Outcome measures Functional status and QOL as assessed by the Spinal Cord Independence Measure version 3 and WHOQOL-BREF questionnaire one year following the injury, respectively.Results A total 37.9% of individuals fulfilled proposed criteria for home-based rehabilitation after acute care. As expected, this group was significantly younger, experienced lesser comorbidities and acute complications, and showed higher motor and sensory function compared to the IFR group. Home-rehabilitation was associated with a higher long-term functional status, physical and psychological QOL, when accounting for relevant confounding factors after an acute AIS-D tSCI. There was no readmission due to failure of home-based rehabilitation.Conclusion Home-based rehabilitation in selected individuals sustaining an acute AIS-D tSCI is a safe and interesting strategy to optimize the long-term outcome in terms of functional recovery, physical and psychological QOL, as well as to optimize inpatient rehabilitation resources. The proposed eligibility criteria can be used by the acute care team to select the optimal discharge orientation in this important subpopulation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".