A Comparison of Two Extraction Methods for Food Oxalate Assessment
Bibliographic record
Abstract
Hyperoxaluria is a primary risk factor for the formation of calcium oxalate-containing kidney stones. Increased dietary oxalate intake and/or intestinal absorption may provide the critical quantity of additional oxalate that triggers the formation of kidney stones. The accurate determination of food oxalate is highly dependent on oxalate extraction, the first step in oxalate analysis. Potential problems include the possibility of elevated oxalate due to in vitro conversion from various oxalate precursors such as ascorbate and failure to dissolve all pre-existing calcium oxalate crystals. The primary objective was to compare the efficiency of the hot and cold extraction methods in extracting oxalate from 50 dry herb and 10 fresh fruit samples. Regardless of the method of extraction, leaves of Atriplex halimus and kiwifruit exhibited the highest concentrations of both total and soluble oxalate among the herbs and the fruits, respectively. The hot extraction method appeared to extract more total oxalate compared to the cold extraction method while there was no significant difference between the methods in efficiency of extracting soluble oxalate. The overall data suggested that the use of the hot acid method will yield a more accurate assessment of the total oxalate content of foods.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".