Bibliographic record
Abstract
The p16 gene belongs to INK4 family of genes and is made up of four members: p16 INK4A , p15 INK4B , p18 INK4C and p19 INK4D , all of which share biological properties, namely, inhibition of cell growth and tumour suppression. After p53 , p16 is the second most common tumour suppressor gene. It has been regarded as the familial melanoma gene. Immunohistochemistry for p16 has a well-defined role in distinct pathological scenarios. It is used to distinguish desmoplastic melanoma from reactive fibrous proliferation, with former showing strong nuclear positivity. In other types of melanoma, p16 protein expression is lost. Spitz nevi show retention of nuclear staining for p16. Benign mesothelial proliferations tend to retain nuclear p16 immunoreactivity, while malignant mesotheliomas lose expression. However, p16 fluorescent in-situ hybridisation analysis is recommended in the workup of malignant mesothelioma. Another common application of p16 immunohistochemistry is as an indicator for human papillomavirus (HPV) infection and p16 protein is overexpressed in HPV-associated tumours. In this context, p16 immunopositivity should be strong, diffuse, nuclear or nuclear and cytoplasmic in location. Another use for p16 is demonstration of p16 immunopositivity in well-differentiated and dedifferentiated liposarcoma.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.010 |
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".