Death by Opioids: Are there non-addictive scientific solutions?
Bibliographic record
Abstract
In the face of the current Opioid crisis in America killing close to 800,000 people since 2004, we are proposing a novel approach to assist in at least attenuating these unwanted premature deaths. While we applaud the wonderful efforts of our governmental institutes and professional societies (NIDA, NIAAA, ASAM, ABAM ) in their extraordinary efforts in combating this continued dilemma, the current approach is failing, and other alternative approaches should at least be tested. These truths present a serious ethical dilemma to scientists, clinicians and counselors in the Reward Deficiency Syndrome (RDS) treatment community. It is important to realize that the current DSM-5 does not actually accurately display the natural brain reward process. The human brain has not been designed to carve out specific drugs like opioids, alcohol, nicotine, cocaine, benzodiazepines or cannabis and process addictions such as gambling as distinct endophenotypes. This is true in spite of natural ligands for cannabinoids, endorphins, or even benzodiazepines. The most accurate endophenotype is indeed reward dysfunction (e.g hypodopaminergic or hyperdopaminergic). With this mind, we are hereby proposing that the current Medication Assisted Treatment (i.e. 'MAT') expands to needed individuals as an initial "Band-Aid" to reduce harm avoidance, with the long-term goal of prophylaxis. So, to be clear, there may be other promising modalities other than MAT such as repetitive transcranial magnetic stimulation (rTMS), exercise and even new medications with positive allosteric modulators of GABA-A receptors, as well as the highly researched Genetic Addiction Risk Score (GARS) coupled with precision KB220Z. This will induce "dopamine homeostasis" to effectively rebalance and restore healthier brain function by promoting the cross talk between various brain regions (e.g. Nucleus accumbens, cingulate gyrus, hippocampus etc.) resulting in dopamine homeostasis. Our laudable goal is to not only save lives, but to redeem joy and improve the quality of life in the recovery community through scientifically sound natural non-addicting alternatives.
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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 | 0.019 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.009 | 0.024 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.014 | 0.023 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".