Weight Loss Precedes Cancer-Specific Symptoms in Pancreatic Cancer-Associated Diabetes Mellitus
Bibliographic record
Abstract
OBJECTIVES: New-onset diabetes mellitus (DM) may herald pancreatic cancer (PaC). We determined whether changes in body weight distinguished PaC-associated DM (PaCDM) from type 2 DM. METHODS: Among Olmsted County residents, we identified 29 PaCDM and 43 type 2 DM subjects who had serial fasting blood glucose measurements, new-onset DM, and no cancer-specific symptoms at DM onset. We compared body weight (kg) and fasting blood glucose (mg/dL) at DM onset, 1 to 2 years before and at index date in the 2 groups. RESULTS: Fasting blood glucose values were similar before and at the onset of DM. Before onset of DM, PaCDM and type 2 DM subjects had similar body weight (P = 0.80). However, at onset of DM, 59% of PaCDM subjects lost weight versus 30% of type 2 DM subjects (P = 0.02). At onset of DM, 56% of type 2 DM subjects gained weight versus 31% of PaCDM subjects (P = 0.04). By index date, PaCDM subjects lost more weight than type 2 DM subjects did (8.3 ± 8.3 vs 0.8 ± 4.8 kg, P < 0.01). CONCLUSIONS: Although new-onset primary type 2 DM is typically associated with weight gain, weight loss frequently precedes onset of PaCDM. The paradoxical development of diabetes in the face of ongoing weight loss may be an important clue to understanding the pathogenesis of PaCDM.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".