Biochemical Assessment of Niacin Deficiency Among Carcinoid Cancer Patients
Bibliographic record
Abstract
OBJECTIVE: Carcinoid cancer patients often have elevated levels of serotonin or its precursor 5-hydroxytryptophan. Normally, serotonin synthesis accounts for a small fraction of tryptophan catabolism, which should be directed along a pathway that allows partial conversion to niacin; hence, increased diversion of tryptophan toward serotonin could cause variable degrees of niacin deficiency in carcinoid patients. Therefore, the prevalence of niacin deficiency among carcinoid patients was investigated by clinical assessment of pellagra and biochemical assessment of whole blood niacin number, a ratio derived from two biologically active forms of niacin (NAD/NADP x 100). METHODS: Clinical and biochemical niacin status were assessed in a cohort of newly diagnosed carcinoid patients with carcinoid syndrome (CCS, n = 36), carcinoid patients without carcinoid syndrome (CWCS, n = 32) and noncarcinoid controls (n = 24) recruited at two primary care clinics. Other aspects of serotonin metabolism were measured by analyses of plasma serotonin and tryptophan and urinary excretion of 5-hydroxyindoleacetic acid. RESULTS: Biochemical niacin deficiency (niacin number < 130) was significantly more common in CCS patients (10 out of 36) compared to controls (p < 0.05, Fisher's exact test), while CWCS patients displayed an incidence that was not significantly elevated (4 out of 32). Only one CCS patient, who was also identified biochemically as niacin deficient, was clinically diagnosed with pellagra. CONCLUSION: Biochemical niacin deficiency is more prevalent among newly diagnosed CCS patients than in controls. Manifestation of pellagra is a less sensitive indicator, and dependence on this endpoint could lead to a lack of appropriate nutritional support for this group of 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 | 0.001 |
| 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) | 0.002 | 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".