Bibliographic record
Abstract
This paper explores the potential use of endocannabinoidome molecules as a therapeutic approach to treating traumatic brain injury (TBI). Google Scholar was used to obtain the primary research literature analyzed for this review. Studies which manipulate the endocannabinoid system through methods such as administration of 2-AG or AEA ligands, inhibiting breakdown enzymes, and using CB1 and CB2 agonists or antagonists have shown promising results in treating TBI; however, no pragmatic clinical therapy has been found so far. The discovery of similar molecules and receptors has resulted in the expansion of the endogenous system and bred the term endocannabinoidome, which incorporates the newly discovered molecules and receptors. Ligands of the endocannabinoidome produce similar therapeutic benefits for TBI but act by different receptor pathways, which may allow one to overcome current existing problems of manipulating the endocannabinoid system for TBI treatment. Currently, therapies used to treat TBI have many unwanted side effects, establishing the need for alternative research options. This paper examines three of these endocannabinoidome molecules that have been previously researched for treating TBI and illuminates their specific receptor pathways and how these receptor pathways operate differently from the ordinary pathways of the endocannabinoid system. Gaining an understanding of the receptor pathways used by endocannabinoidome molecules will open a new field of research for therapeutics to treat TBI.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".