Meeting report of the International Consortium of Stem Cell Networks’ Workshop Towards Clinical Trials Using Stem Cells for Amyotrophic Lateral Sclerosis/Motor Neuron Disease
Bibliographic record
Abstract
The International Consortium of Stem Cell Networks' (ICSCN) Workshop Towards Clinical Trials Using Stem Cells for Amyotrophic Lateral Sclerosis (ALS)/Motor Neuron Disease (MND) was held on 24-25 January 2011. Twenty scientific talks addressed aspects of cell derivation and characterization; preclinical research and phased clinical trials involving stem cells; latest developments in induced pluripotent (iPS) cell technology; industry involvement and investment. Three moderated panel discussions focused on unregulated ALS/MND treatments, and the state of the art and barriers to future progress in using stem cells for ALS/MND. This review highlights the major insights that emanated from the workshop around the lessons learned and barriers to progress for using stem cells for understanding disease mechanism, drug discovery, and as therapy for ALS/MND. The full meeting report is only available in the online version of the journal. Please find this material with the following direct link to the article: http://www.informahealthcare.com/als/doi/10.3109/17482968.2011.590992 .
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 | 0.016 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.029 | 0.011 |
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".