Bibliographic record
Abstract
Pain is a protective sensation, but it can also be a burden without any useful value. Pain as a friend warns of impending damage and protects the body from injury. Pain as a foe is a useless sensation that makes the underlying problem worse and becomes a disease in its own right. Mechanistically, the systems that mediate good pain and bad pain are often the same, with bad pain being the result of such mechanisms being triggered inappropriately, by irrelevant stimuli or with a time course and intensity disproportionate to the originating cause. We are beginning to know more about the neurobiology of bad pain. The relevant mechanisms are often linked to dysfunction or disease of the nervous system, either of the peripheral nerves or of the central nervous system itself. For example, under normal conditions, activity in large, myelinated A[beta]-fibers inhibits nociceptive primary afferent inputs to the central nervous system. However, in inflammatory and neuropathic conditions, these actions are reversed, leading to touch-evoked pain or tactile allodynia. The mechanism responsible for this reversal is a change in the synaptic actions of [gamma]-aminobutyric acid that switches from being an inhibitory neurotransmitter to an excitatory one. Our challenge was to devise methods for pain relief based on elimination of the useless aspects of pain and the restoration of the protective qualities of normal pain sensation.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.014 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".