Bibliographic record
Abstract
The pace of molecular discovery relevant to nerve regeneration has accelerated. New insights into regeneration, however, have not necessarily been partnered with rigorous approaches to measure regeneration. The purpose of this chapter is to engender readers with a healthy appreciation of new findings, based on rigorous approaches, that confirm the complexity and beauty of the regenerative process. Similarly, the reader should be skeptical of approaches that do not live up to that standard. Assays of regeneration should ideally encompass all of the crucial steps involved in the regenerative timetable: early sprouting, axon elongation, regrowth of axon radial caliber or girth, remyelination of larger caliber axons, repopulation of nerve trunks by mature axons, and extension to target tissues. During regeneration axons regain electrophysiological properties that they have lost, features that can be carefully assayed. Finally, it is critical to know whether there has been a resumption of function, addressed through “functional” or behavioral endpoints. This chapter presents a summary of regeneration assays and a discussion of their strengths and limitations. Structural (histological) approaches Few other measures can convey the structural beauty of regeneration captured in a high-quality histological snapshot. Histological techniques demand strict attention toward the details of specific protocols and they require optimal handling of specimens that are appropriately sampled. Their exactitude sets a standard of quality that is enormously satisfying. Unfortunately, classical histological approaches are frequently dismissed and substituted with easier or more colorful techniques that have lower resolution.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.032 | 0.017 |
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".