Bibliographic record
Abstract
The cannabinoids, Δ9 tetrahydrocannabinol and its analogue, nabilone, have been found to reliably attenuate the intensity and frequency of post-traumatic nightmares. This essay examines how a traumatic event is captured in the mind, after just a single exposure, and repeatedly replicated during the nights that follow. The adaptive neurophysiological, endocrine and inflammatory changes that are triggered by the trauma and that alter personality and behavior are surveyed. These adaptive changes, once established, can be difficult to reverse. But cannabinoids, uniquely, have been shown to interfere with all of these post-traumatic somatic adaptations. While cannabinoids can suppress nightmares and other symptoms of post-traumatic stress disorder, they are not a cure. There may be no cure. The cannabinoids may best be employed, alone, but more likely in conjunction with other agents, in the immediate aftermath of a trauma to mitigate or even abort the metabolic changes which are set in motion by the trauma and which may permanently alter the reactivity of the nervous system. Steps in this direction have already been taken.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".