Periocular sebaceous gland carcinoma: do androgen receptor (<scp>NR</scp>3<scp>C</scp>4) and nuclear survivin (<scp>BIRC</scp>5) have a prognostic significance?
Bibliographic record
Abstract
PURPOSE: The study aimed at evaluating the expression of androgen receptor (AR) and nuclear survivin (NS) in periocular sebaceous gland carcinoma (SGC) and to determine whether this expression is associated with histopathological features, markers of apoptosis and proliferation and with clinical outcomes. METHODS: This was a retrospective, comparative case series which included 56 patients with a biopsy-proven periocular SGC. Immunohistochemical staining for AR, survivin, p53 and Ki-67 was analysed in all cases. RESULTS: All patients expressed AR, p53 and Ki-67 in the nucleus of tumour cells. Twenty-four patients (42.8%) had a high AR score, and 32 patients (57.2%) had a low AR score. Twenty-four (42.8%) patients expressed survivin in the nucleus of tumour cells. Nine (37.5%) had a high NS score, and 15 (62.5%) had a low NS score. Patients with a high AR score had a greater recurrence (p < 0.005), higher expression of Ki-67 (p < 0.0001) and a lower p53 expression (p < 0.005). Nuclear expression of survivin correlated with a high Ki-67 labelling index (0.0001) and low p53 expression (<0.005). Neither nuclear expression of survivin nor the NS score correlated with any clinicopathological features. CONCLUSION: Expression of AR significantly impacts prognosis and is thus promising prognostic marker in periocular SGC.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Immunohistochemical biomarker study in sebaceous gland carcinoma; clinical pathology.
This studies prognostic markers in carcinoma, not research practice.
Clinical oncology study of prognostic markers in periocular sebaceous carcinoma.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".