Bibliographic record
Abstract
Most neuroscientists measure neuronal activity in the brain to predict or explore the contribution of neurons to physiological/pathological functions of the brain. For example, if an increase in neuronal activity is detected in one brain region by peripheral noxious stimuli, we call this area the pain region or pain matrix. In the case of activity detected during conscious processes, such as placebo treatment, we label these regions as responsible for the placebo treatment. Due to limited access to the conscious brain and the reductionist nature of most modern neuroscience, we are all reluctant to explore more sophisticated hypotheses. Few studies have performed experiments at neuronal network levels. Like other higher order brain functions, the investigation into the neuronal mechanisms of placebo analgesia is proving to be difficult for clinicians and basic researchers. For example, there are individual differences in the response to placebos, called responders and non-responders. Certain populations of individuals show a significantly greater response to placebos than others. Evidence for environmental and social impacts on brain development is increasing; therefore, individual differences in higher order brain functions such as placebo analgesia is expected. Levine and colleagues (1978) performed a classic study on placebo analgesia [1]. Their hypothesis is based on the discovery of endogenous opioid peptides in the central nervous system. Placebo analgesia in patients was blocked by naloxone, an opioid receptor antagonist. These experiments indicate that the endogenous analgesia systems are likely to be activated during placebo analgesia, possibly at different levels of the central nervous system.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".