Is increasing the pulse width from 0.5 to 1 ms an effective strategy to optimize clinical and electrical outcomes in bilateral <scp>ECT</scp> treatment?
Bibliographic record
Abstract
accessibility to treatment, and societal pressure to drink might as well be considered as environmental factors.Such drinking culture may familiarize alcohol-sensitive persons with alcohol, and even overcome the genetic protection of the higher acetaldehyde by ALDH2*2/*2.This speculation from these small case descriptions seems consistent with the sales data of RTDs on a national level.Interestingly, while the overall consumption of alcohol has gradually declined, that of liqueurs mostly consisting of RTDs has substantially increased for the past 20 years (Table S1), 6 and the prevalence of AUD is also on the rise (1.6% (2003), 3.4% ( 2016)).7,8 In addition, the prevalence of ALDH2*2 carriers admitted to our center showed significant associations with consumptions of total alcohol and liqueurs as well as the liqueurs/total alcohol ratio (Table S2).These facts suggest that the distribution of ALDH2 phenotypes among patients with AUD has been changing recently.This study had a limitation.Clinical characteristics relevant to the development of AUD, including preferences about alcoholic beverages and illness severity and duration were lacking.Although such socio-cultural factors among Japanese cannot be necessarily extrapolated to other populations, physicians are advised to be aware that any environmental situations could trigger the onset of developing AUD even among genetically alcohol-sensitive individuals.This notion is especially relevant in the light of reported increased drinking in the COVID-19 pandemic environment.9
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".