D(—)–Lactic Acid Producing Probiotics, D(—)–Lactic Acidosis and Infants
Bibliographic record
Abstract
There is mounting evidence that ingestion of selected probiotics can modify disease morbidity for specific conditions affecting humans, and there is growing interest in the amelioration or prevention of disease with probiotics. Modulation in gene expression of the cellular elements of the intestinal mucosa and interbacterial interactions are leading theories as to the mechanism whereby probiotics can effect benefit for the host. Furthermore, gene-environmental interactions are considered to be important in the development of disease in those at genetic risk. With the intestinal tract harbouring large numbers of bacteria, alteration of the microbial environment with probiotic microbes is being considered as a controllable factor that may limit disease expression for those at genetic risk. This reasoning has led to interest in the administration of probiotics to infants. However, there are significant developmental changes occurring in many organ systems from the time of parturition and during the first months of life. Because there is little in the published scientific medical literature regarding the effects of long-term administration of probiotics to infants, potential problems must be considered; one such issue is that of administration of D(-)-lactate-producing probiotics. An appraisal of the current knowledge of this potential adverse effect is the subject of this communication.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".