L-carnosine as an enhancer to computerized cognitive behaviour therapy in Japanese workers, an unjustified claim
Bibliographic record
Abstract
This letter comments on the conclusion drawn by Shirotsuki et al. (2017) in their article entitled “The effect for Japanese workers of a self-help computerized cognitive behaviour therapy program with a supplement soft drink”, recently published in BioPsychoSocial Medicine . The authors concluded that their drink, containing L-carnosine, enhances the effects of a computerized cognitive-behavioural therapy (CCBT) on the psychological well-being of healthy Japanese workers. Yet, we argue that their conclusion is unfounded given their results and the methodological shortcomings of their study. Briefly, while the authors reported improvement on the tension-anxiety subscale of the Profile of Mood States (POMS) in the CCBT only group, they also observed a lack of improvement on this subscale in the CCBT+L-carnosine group suggesting that the drink washes out this beneficial effect of CCBT. Methodological issues include the uncontrolled levels of L-carnosine metabolized by participants jeopardize the study’s internal validity. Also, the clinical meaningfulness of the findings seems dubious as post-treatment scores remained within the range of the general Japanese population. Consequently, we argue that Shirotsuki et al.’s study should be re-conducted before drawing any valid conclusion.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".