Bibliographic record
Abstract
The identification of nonhematopoietic progenitor cells in cord blood has spawned great interest in using cord blood cells for new indications in regenerative therapy. Many preclinical studies demonstrated improvement in reperfusion and markers of organ recovery using cord blood-derived cells in a range of animal models. Initial results heralded increasing clinical interest regarding the use of cord blood for regenerative therapy. Initial clinical studies were largely uncontrolled feasibility studies that were case series and reported on small numbers of patients. The emergence of controlled studies has been slower, although multiple controlled studies have been conducted in patients with cerebral palsy and type I diabetes. Heterogeneity in the cellular product, patients, study design, and the timing of outcome measurements remains barriers to meta-analysis and a clearer understanding of efficacy. Controlled studies of modest size have been reported for a range of additional conditions. The conduct of controlled clinical trials to evaluate potential new uses of cord blood for regenerative therapy remains essential. None of the indications studied to date can be regarded as proven. Moreover, consistency in outcome reporting in terms of the instruments used and the time points for assessment after therapy are needed, including longer follow-up of study participants. Frequent and careful evaluation of the evidence will allow cord blood banks, health care providers, and patients to assess potential new options in the use of cord blood for regenerative therapy.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".