Exploring Serum Pyridoxal 5’-Phosphate (Vitamin B6) levels in Head and Neck Cancer: A Systematic Review
Bibliographic record
Abstract
IntroductionDietary imbalances or deficiencies in nutrients may affect DNA replication, repair, and regulation, potentially facilitating cancer development.Diet intervention can improve cancer outcomes without introducing additional toxicities and longterm complications.Vitamins play a crucial role in modulating the effects of healthy dietary uptake for an individual.The transformation of lipids, amino acids, carbohydrates, and nucleic acids is aided by vitamin B6.An absence of vitamin B6 has been linked to a higher chance of developing several chronic illnesses, such as cancer, heart disease, and cognitive loss. AimThis systematic review aimed to evaluate the association between serum vitamin B6 levels and the risk of developing head, neck, and oesophageal cancer. Materials & MethodsAccording to this Systematic Review, the initial search was conducted on Databases such as PubMed, Google Scholar and Science Direct.The articles were searched between 2000 to 2022.The Risk of bias was estimated by the Newcastle-Ottawa Scale.The keywords which were used in various combinations were Serum Vitamin B6; Head and Neck Cancer; Head and Neck Carcinoma.The search was clarified using the operator 'AND' to achieve the desired results.The electronic search was performed independently by two researchers. ResultsAfter the removal of duplicates and title and abstract screening, three studies met the inclusion criteria.Two studies showed a low risk of bias demonstrating as good quality and one study showed a high risk of bias.The data concluded that in two articles low levels of serum PLP were noted in Head and neck cancer cases while one article, showed a null association. ConclusionA significant association between low Serum Vitamin B6 (Pyridoxal-5'-Phosphate) levels and increased risk of Head and Neck Squamous Cell Carcinoma (HNSCC).More reviews including more studies and meta-analyses are required to validate the results of this systematic review.
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".