Effect of Blending and the Simultaneous Ingestion of a Probiotic Containing Oxalate-Degrading Bacteria on Oxalate Absorption
Bibliographic record
Abstract
Both a high dietary oxalate intake and increased gastrointestinal absorption can lead to elevated urinary oxalate, a risk factor for kidney stone formation. Numerous studies have assessed whether daily ingestion of a probiotic containing oxalate-degrading bacteria can reduce urinary oxalate/oxalate absorption, but it appears only one previous study assessed whether the simultaneous ingestion of oxalate-degrading probiotic bacteria consumed with an oxalate load can exert this effect. This was assessed in the present study in a population of 11 healthy non-stone formers (6 males, 5 females), aged 21 – 37 y, using the probiotic VSL#3®. A spinach-sweet potato mixture provided an oxalate dose of 534 mg and urine samples were collected for a 22 h period post-oxalate ingestion. An additional objective was to assess the effect of blending oxalate-containing foods on oxalate absorption. The overall results suggested that the spinach and sweet potato provided oxalate of low bioavailability. Changing the texture of these foods by blending did not have an effect on oxalate absorption nor was VSL#3® effective in reducing urinary oxalate levels. VSL#3® may have been more effective if the oxalate dose had been provided in a more bioavailable form leading to a higher initial oxalate absorption/urinary oxalate excretion.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".