Proof‐of‐concept study: Evaluation of plasma and urinary electrolytes as markers of response to L‐asparaginase therapy in dogs with high‐grade lymphoma
Bibliographic record
Abstract
Response to chemotherapy is one of the most important prognostic factors in dogs with lymphoma. The objective of this feasibility study was to evaluate if clinical responses to a specific cytotoxic agent (L-asparaginase) could be anticipated by measuring analyte concentrations in plasma and urine concentrations of lymphoma-bearing dogs. We hypothesized that potassium and phosphate concentrations in plasma and urine would be higher in dogs that completely responded to therapy. Plasma and urine samples of dogs with lymphoma were obtained before 12 and 24 hours after intramuscular L-asparaginase injections. Peripheral lymph node volumes were evaluated according to the Veterinary Cooperative Oncology Group standardized criteria. Plasma and urine electrolyte, calcium, phosphate, creatinine, urea, total protein, and albumin concentrations were measured, and the fractional excretions of each electrolyte were calculated. Statistical analyses compared complete vs partial responders using a linear regression model. Contrast analyses were also performed to differentiate the mean of each group, with adjustments made with the Benjamini-Hochberg procedure. Fourteen dogs were included, eight with complete responses, and six with partial responses. Plasma phosphate concentrations were significantly higher at 12 hours (P = .0003) and 24 hours (P = .009) after complete responses to therapy. This study demonstrates the potential use of plasma and urine analyte monitoring after chemotherapy induction. Plasma phosphate measurements represent a potential indicator of early responses to L-asparaginase therapy. Larger population studies are warranted to confirm these preliminary results.
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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 | 0.004 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".