Influence of a multistrain probiotic on body composition and mood in female occupational shift workers
Bibliographic record
Abstract
This study sought to investigate the effects of a multistrain probiotic on body composition, regional adiposity, and a series of associated metabolic health outcomes. Female health care workers employed on a rotating-shift schedule (n = 41) completed baseline anthropometric assessments; a fasted blood draw; questionnaires to assess anxiety, depression (Hospital Anxiety and Depression Scale), and fatigue (Chalder Fatigue Survey); and an exercise fatigue test. Identical post-tests occurred following 6 weeks of daily supplementation with placebo (PLA) or probiotics (2.5 × 109 CFU/g) containing 9 bacterial strains (PRO; Ecologic Barrier) combined with a prebiotic carrier matrix. PRO attenuated fat mass increases (change (Δ), 0.14 kg; confidence interval (CI) –0.46 to 0.75 kg) compared with PLA (Δ, 0.79 kg; CI 0.03–1.54 kg), whereas modest reductions in visceral adiposity resulted for both PRO and PLA. Metabolic biomarkers (total cholesterol, high-density lipoprotein, glucose, adiponectin, C-reactive protein, interleukin-6, leptin) were not influenced by either treatment (p > 0.05). Nonsignificant, but potentially clinically relevant, improvements in anxiety (Δ, –2.3 ± 2.63) and fatigue (Δ, –4.8 ± 5.5) were observed with PRO; exercise performance was unaffected. Results indicate a potential protective effect of probiotics against fat mass gain. Probiotics may alleviate anxiety and fatigue in shift-working females.
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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 | 0.000 |
| 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".