A diet with a struvite relative supersaturation less than 1 is effective in dissolving struvite stones<i>in vivo</i>
Bibliographic record
Abstract
Magnesium ammonium phosphate (struvite) is one of the most common minerals found in feline uroliths. Previous studies have shown the efficacy of acidifying calculolytic diets (inducing urine pH < 6.5), in dissolving struvite stones in cats. Recent work in our laboratory found that wet and dry test diets induce a struvite urinary relative supersaturation (RSS) < 1 and that the urine of healthy cats fed the dry test diet dissolved feline struvite stones in vitro. The objective of the present study was to demonstrate the efficacy of those test diets on naturally occurring struvite urocystoliths in cats. A total of twenty-one cats were used, of which seventeen completed the study. Of the seventeen cats, eight were fed the wet test diet and nine the dry test diet. Uroliths dissolved in a median of 18 (10-55) d. In the remaining four cats, uroliths failed to dissolve and were removed surgically. Quantitative analysis showed that these uroliths contained either calcium oxalate or calcium phosphate. The present study demonstrates that diets that induce a struvite RSS < 1 result in struvite stone dissolution in vivo.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".