Urinary iodine clearance after iodinated contrast administration to healthy cats
Bibliographic record
Abstract
BACKGROUND: I) by the thyroid gland. This has potential implications for the treatment of cats with hyperthyroidism that have recently undergone computed tomography (CT) with IV administration of iodinated contrast medium (ICM). HYPOTHESIS: To determine the time to normalize urinary iodine clearance after administration of ICM. We hypothesized that it would require 4 weeks for urinary iodine concentration (UIC) to decrease to baseline after IV administration of ICM. ANIMALS: Ten healthy adult neutered male cats. METHODS: All cats were sedated and received Iopamidol at a dose of 2 mL/kg (600 mg/kg). Urinary iodine and creatinine concentrations were measured before administration of Iopamidol and on days 1, 2, 3, 7, 10 and weeks 2 to 6 after administration. The urinary iodine-to-creatinine ratio (UICR) was calculated. Outcome variables were modeled using a linear mixed-effects model. RESULTS: Urinary iodine concentration increased 37- to 884-fold on Day 1 after ICM injection and returned to baseline during Week 2. Compared with baseline, UIC was significantly increased for Days 1 to 7 (all P < .001); UC was significantly lower for Days 1 to 10 (all P < .03); and UICR was significantly increased from Days 1 to 10 (all P < .001, except Day 10 P = .05). CONCLUSIONS: I treatment could be appropriate but needs to be confirmed in hyperthyroid cats.
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 | 0.002 |
| 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 | 0.000 |
| 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".