Spurious hyperbicarbonatemia and a negative anion gap in a cat and a dog with severe rhabdomyolysis
Bibliographic record
Abstract
A 3-year-old male neutered domestic shorthair cat and a 2-year-old male neutered Labrador-mix dog were separately presented to the Veterinary Medical Center for evaluation after sustaining significant muscle trauma due to a dog attack and seizure activity, respectively. In both cases, biochemical analysis was consistent with rhabdomyolysis. Additionally, a markedly increased measured serum bicarbonate concentration and negative calculated anion gap were observed. As these biochemical abnormalities were not expected and deemed incompatible with life, an interference with the analyzer measurement of bicarbonate involving marked increases in pyruvate and lactate dehydrogenase (LDH) following myocyte injury was suspected. Venous blood gas analysis calculated bicarbonate concentration and anion gap were within reference interval, while measured LDH activity was markedly increased. These findings supported an analyzer-generated interference. This is the first published report of a previously described chemistry analyzer interference of markedly increased LDH activity with serum bicarbonate concentration measurement in dogs and cats. Awareness of this interference is important, particularly in the emergency setting, as it may influence case management.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".