Making Neurons from Human Stem Cells
Bibliographic record
Abstract
Neurons are cells contained within the brain and spinal cord that specialize in communicating information within the body. Neurons are important for many things including moving, breathing, thinking, and feeling pain. If these cells are injured due to an accident, for example, the body can no longer perform some of these important functions. As a result, a person can become disabled in some way. To help patients with injuries to their brains or spinal cords, scientists and doctors may be able to replace damaged neurons by transplanting new cells into the injured person. By using new cells to replace the neurons lost from injury, it is possible that patients will recover some of their lost abilities, such as moving. Scientists think that stem cells are the ideal cell type to transplant into injured patients, because stem cells can multiply and change into the different cell types needed to repair the injury. The stem cells that researchers transplant can be made in the lab from skin cells and blood cells. Skin and blood cells can both be obtained using a needle. Currently, stem cells from patients with brain disease, like Alzheimer’s disease, are used to study these diseases in the laboratory so that cell replacement therapies can be developed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.009 |
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".