Evaluation of clinical experience using cell-based therapies in patients with spinal cord injury: a systematic review
Bibliographic record
Abstract
OBJECT: Using a systematic approach, the authors evaluated the current utilization, safety, and effectiveness of cellular therapies for traumatic spinal cord injuries (SCIs) in humans. METHODS: A systematic search and critical review of the literature published through mid-January 2012 was performed. Articles included in the search were restricted to the English language, studies with at least 10 patients, and those analyzing cellular therapies for traumatic SCI. Citations were evaluated for relevance using a priori criteria, and those that met the inclusion criteria were critically reviewed. Each article was then designated a level of evidence that was developed by the Oxford Centre for Evidence-Based Medicine. RESULTS: The initial literature search identified 651 relevant articles, which decreased to 350 after excluding case reports and reviews. Evaluation of articles at the title/abstract level, and later at the full-text level, limited the final article set to 12 papers. The following cellular therapies employed in humans with SCI are reviewed: bone marrow mesenchymal and hematopoietic stem cells (8 studies), olfactory ensheathing cells (2 studies), Schwann cells (1 study), and fetal neurogenic tissue (1 study). Overall the quality of the literature was very low, with 3 Grade III levels of evidence and 9 Grade IV studies. CONCLUSIONS: Several different cellular-mediated strategies for adult SCI have been reported to be relatively safe with varying degrees of neurological recovery. However, the literature is of low quality and there is a need for improved preclinical studies and prospective, controlled clinical trials.
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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 | 0.021 | 0.089 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.007 |
| Bibliometrics | 0.018 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".