Prognostic factors of head and neck cutaneous squamous cell carcinoma: A systematic review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Head and neck cutaneous squamous cell carcinoma (HNCSCC) is a non-melanoma skin cancer that is mostly caused by solar ultraviolet radiation exposure. While it usually has an excellent prognosis, a subset of patients (5%) develops nodal metastasis and has poor outcomes. The aim of this study was to systematically review the literature and evaluate the prognostic factors of HNCSCC in order to better understand which patients are the most likely to develop metastatic disease. METHODS: A comprehensive literature search was performed on PubMed and EMBASE to identify the studies that evaluated the prognostic factors of HNCSCC. Prognostic factors were deemed significant if they had a reported p-value of < 0.05. Proportions of studies that reported a given factor to be statistically significant were calculated for each prognostic factor. RESULTS: The search yielded a total of 958 citations. Forty studies, involving a total of 8535 patients, were included in the final analysis. The pre-operative/clinical prognostic factors with the highest proportion of significance were state of immunosuppression (73.3%) and age (53.3%); while post-operative/pathological prognostic factors of importance were number of lymph nodes involved with carcinoma (70.0%), margins involved with carcinoma (66.7%), and tumor depth (50.0%). CONCLUSION: This systematic review is aimed to aid physicians in assessing the prognosis of HNCSCC and identifying the subsets of patients that are most susceptible to metastasis. It also suggests that immunosuppressed patients with a high-risk feature on biopsy, such as invasion beyond subcutaneous fat, could possibly benefit from a sentinel lymph node biopsy.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it