Câncer de pele não melanoma: revisão integrativa
Bibliographic record
Abstract
Introduction: Non-melanoma skin cancer comprises a group of neoplasms with a high incidence in the world population. It is divided into basal cell carcinoma and squamous cell carcinoma. As it is highly prevalent, understanding the process of oncogenesis and the relationship with ions, proteins and cellular receptors in nonmelanoma skin cancer can contribute to the evaluation of new therapies. Objective: To understand the oncogenesis process of non-melanoma skin tumors and its relationship with the immunolocalization of IP3R. Methods: Integrative literature review with evidence synthesis. The database was PubMed; the search strategy: “squamous cell carcinoma, AND/OR basal cell carcinoma, AND/OR IP3R, AND/OR immunohistochemistry”. Works published between 2018 and 2023 were considered for review; 40 works were included, fully read and summarized. Results: Non-melanoma skin cancer is the most common malignant tumor worldwide, with 75-80% being basal cell carcinoma, and up to 25% being cell carcinoma. Molecular interactions in general involve a large participation of tumor suppressor molecules, as well as procto-oncogenes. Furthermore, voltage-dependent ion channels control the cytosolic flow of ions, including calcium. The IP3R (phosphatidyl inositol-3 receptor) allows the exit of calcium from the endoplasmic reticulum so that it can be used by the cell for physiological activities such as proliferation, angiogenesis, motility and invasion capacity. Conclusion: The IP3R, due to its immunohistochemical expression characteristics, appears may also be related to the pathophysiology of nonmelanoma skin cancer.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".