Evaluation of respiratory compensation to metabolic acid-base disturbances in cats
Bibliographic record
Abstract
Syre-base-forstyrrelser identificeres ofte hos kritisk syge patienter, og evaluering af syre-base-status er derfor i disse tilfælde blevet en almindelig del af den kliniske udredning. Baseret på meget begrænset eksisterende litteratur mistænkes det, at katte ikke udvikler den respiratoriske kompensation for en metabolisk syre-base-forstyrrelse, som er normal for mennesker og hunde. For at kunne diagnosticere og behandle katte med metaboliske syre-base-forstyrrelser korrekt, er det vigtigt at vide, om og i hvilken grad respiratorisk kompensation kan forventes. Formålet med dette studie var at bestemme, hvorvidt respiratorisk kompensation kan detekteres ved blodgasanalyse for katte med metaboliske syre-base-forstyrrelser. Dette blev undersøgt ved at evaluere blodgasanalyser indsamlet fra katte over de sidste 15 år på Universitetshospitalet for Familiedyr på Københavns Universitet. Syre-base-forstyrrelserne blev klassificeret ved hjælp af pH, Pco2 og HCO3- . Den respiratoriske kompensation for de metaboliske syre-base- forstyrrelser blev evalueret ved at beregne forskellen på patientens målte og forventede Pco2 . Resultatet fra dette studie viste en signifikant forskel på den målte og forventede Pco2 . På denne baggrund vurderes det, at katte ikke udvikler detekterbar respiratorisk kompensation vurderet på blodgaskriterier opsat for hund og menneske.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".