Vitamin B12 status in hospitalised cancer patients: Prevalence and clinical implications of depletion and hypervitaminosis
Bibliographic record
Abstract
BACKGROUND & AIMS: The prevalence and clinical significance of vitamin B12 alterations in patients with cancer are poorly understood. We aimed to assess the prevalence and risk factors of vitamin B12 depletion or hypervitaminosis in patients with cancer. METHODS: We retrospectively included hospitalised patients with cancer in 2017-2022. Plasma B12 levels were stratified as very low (VL, <200 pg/ml), low (L, 200-299 pg/ml), normal (N, 300-812 pg/ml), or high (H, ≥813 pg/ml). We collected demographic and several clinical data (e.g., comorbidities, nutritional status, ECOG-PS, cancer site and stage). Univariate and multivariate analyses for factors associated to the vitamin B12 status were fitted. RESULTS: 788 patients (F/M ratio 1.05, median age 72 years, [25th, 75th percentiles 62, 78 years]) were included. Vitamin B12 was VL in 14.1%, L in 19.4%, N in 49.4%, and H in 17.1% cases. Vitamin B12 distribution increased significantly as function of ECOG-PS levels. Patients with breast cancer were characterized by the highest median B12 value, while colorectal cancer patients by the lowest. Vitamin B12 was also significantly higher in advanced compared to early-stage patients as well as in those who had liver failure. Multivariate analysis showed that the probability of H vs. VL B12 levels was significantly increased in patients with hypoproteinemia, hypo-prealbuminemia, and ECOG-PS≥2, and decreased in those with colorectal and gastric cancer. CONCLUSION: Vitamin B12 impairment is common in cancer patients. Increased vitamin B12 is associated with an impaired clinical status, while vitamin B12 depletion is more common in early-stage cancer and in elderly patients.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".