Abstracts from the XXIII CINP Congress, Montréal, June 23–27, 2002.
Bibliographic record
Abstract
Promising scientific findings have provided new insights into the role of serotonergic agents as treatments for alcoholism.Predicated on the findings that alcoholics may differ in their biological predisposition to disease, exemplified by differences in serotonergic function, we examined whether the serotonin-3 antagonist, ondansetron, would be differentially effective among various types of alcoholic.From an initial sample of 321 alcoholics entered into a double-blind, randomized clinical trial, we observed that ondansetron (1, 4, and 16 mcg/kg b.i.d) was significantly superior to placebo in reducing drinking and enhancing abstinence among biological (i.e.early onset) but not non-biological (i.e. late onset) alcoholics.Preliminary evidence from our laboratory, which also will be presented in this session, has shown that these early onset compared with late onset alcoholics have greater serotonin uptake into platelets.Serotonin uptake in both brain and platelets is regulated by an identical serotonin transporter with functional polymorphic types.It is, therefore, tempting to hypothesize, that functional differences in expression of these polymorphisms may be an important determinant of a therapeutic treatment response to ondansetron.Ongoing developments of this research holds the promise of identifying molecular genetic methods for determining which type of alcoholic would respond best to treatment with a specific type of serotonergic agent.
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) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.541 | 0.247 |
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".