Paraneoplastic hypoglycemia in a diabetic dog with an insulin growth factor‐2–producing mammary carcinoma
Bibliographic record
Abstract
A 6-year-old intact female Labrador Retriever had diabetes mellitus, which had been difficult to control with insulin. The dog also had a solid ductal mammary carcinoma with very rapid growth, which was temporally related to onset of hypoglycemia. Eight months after initial diagnosis of diabetes, the dog had a hypoglycemic crisis. Insulin administration was stopped and serum glucose concentration returned to normal. Three months after discontinuing insulin, another hypoglycemic crisis occurred. During subsequent months, serum glucose concentrations remained at life-threatening levels (1.64-2.12 mmol/L, reference interval 4.44-6.66 mmol/L) simultaneously with an increase in the size of the mammary tumor, which reached a diameter of about 16 cm. At the time of surgery for removal of the tumor serum glucose concentration was 2.20 mmol/L and was then monitored every 3 hours after excision of the tumor. The glucose concentration continued to rise and reached 9.99 mmol/L 12 hours after the removal of the mammary tumor. Immunohistochemical staining demonstrated expression of insulin growth factor-2 by tumor cells, which apparently had caused the hypoglycemia during tumor growth even in a diabetic dog. Hyperglycemia associated with diabetes was pronounced after excision of the tumor and had been masked by the paraneoplastic effect of the tumor.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".