Expired probiotics: what is really in your cabinet?
Bibliographic record
Abstract
The popularity of using probiotics has surged, since they became widely accepted as safe and help improve general health. Inevitably, some of these products are used after expiration when microbial cell viability is below the recommended effective dose. Given that probiotics must be live microorganisms administered in adequate amounts, the aim of this study was to measure viability in expired products and assess how packaging and storage conditions impact efficacy, if at all. Thirty-three expired probiotic products were evaluated, of which 26 were stored in conditions recommended by the manufacturer. The viable microbial cells were enumerated and representative isolates identified by 16S and internally transcribed spacer rRNA gene sequencing. While the products had a mean past expiration time of 11.32 (1-22) years, 22 still had viable contents, and 5 were within or above the original product cell count claim. Product formulation and the number of species present did not appear to impact the stability of the products. However, overall packaging type, storage conditions and time since expiry were found to affect viability. All products with viable cells had the strain stipulated on the label. Despite some selected probiotic products retaining viability past their expiry date (indicating long-term storage is possible), the total counts were mostly well below that required for efficacious use as recommended by the manufacturer. Consuming expired probiotics may not yield the benefits for which they were designed.
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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".