Bibliographic record
Abstract
PURPOSE: To review knowledge on therapeutic uses of ethanol and the latter's effectiveness and safety profiles in a range of indications. METHODS: MEDLINE and PubMed databases were searched for relevant peer-reviewed papers published in English between 1888 and 2018 using the following search terms: ethanol, therapeutic, alcohol withdrawal syndrome, antiseptic, antidote, methanol, ethylene glycol, neurolysis, embolization, cyst, sclerosing agent, sclerotherapy, arteriovenous malformations, ablating agent. Studies providing information about association between alcohol and therapeutic indications, or mechanic explanation for the association were included for review. RESULTS: According to the World Health Organization, approximately three millions deaths worldwide are attributable to alcohol consumption each year. However, the low-to-moderate consumption of ethanol has a number of beneficial effects (mainly on cardiovascular mortality and diabetes). Hence, ethanol has an unusual spectrum of effects that seems interesting for therapeutic purposes. Ethanol's risk-benefit ratio appears to be positive in some therapeutic indications such as antidote to methanol or ethylene glycol poisoning, neurolysis, alcohol withdrawal syndrome, or antiseptic. CONCLUSION: With the development of interventional radio technologies, and thus extremely precise access to anatomical structures, alcohol has been given new indications - particularly as an embolization, sclerosing or ablation agent. Moreover, constant progress in our knowledge of ethanol's pharmacodynamics might highlight other therapeutic indications for this compound in the future. Ethanol's low cost and wide availability make it a valuable therapeutic agent, compared with other reference treatments. Furthermore, ethanol has a long track record of safety and effectiveness in the indications mentioned above.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".