Prognostic effects of previous cancer history on patients with major salivary gland cancer
Bibliographic record
Abstract
OBJECTIVES: To explore the prognostic effects of previous cancer history on patients with major salivary gland cancer (SGC). SUBJECTS AND METHODS: SGC patients with (sec-SGC) and without (one-SGC) a previous cancer from the SEER database were identified. Cox proportional hazards regression (CoxPH) models were used to compare the prognosis between sec-SGC and one-SGC patients. Subgroup analyses for sec-SGC patients by gender, previous cancer types, previous cancer histology, and cancer diagnosis interval (CDI) were performed. Two CoxPH models were constructed to distinguish sec-SGC patients with different prognostic risks. RESULTS: 9098 SGC patients were enrolled. Overall, sec-SGC patients (adjusted HR [aHR] = 1.26, p < 0.001), especially those with a CDI ≤ 5 years (aHR = 1.47, p < 0.001), had worse overall survival (OS) than one-SGC patients. In subgroup analysis, only sec-SGC patients with a previous head and neck cancer who were female (aHR = 2.38, p = 0.005), with a CDI ≤ 5 years (aHR = 1.65, p = 0.007) or with a previous squamous cell carcinoma (aHR = 6.52, p < 0.001) had worse OS. Our models successfully differentiated all sec-SGC patients into high-, intermediate- and low-risk groups with different prognosis. CONCLUSIONS: Sec-SGC patients with different previous cancer types, gender, CDI and previous cancer histology had varied prognosis. The models we constructed could help differentiate the prognosis of sec-SGC patients with different risks.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".