Prognostic factors impacting survival rates of hypopharyngeal cancer with nomogram prediction: a SEER-based study
Bibliographic record
Abstract
Abstract Background We analyzed prognostic factors of hypopharyngeal carcinoma and developed a nomogram to predict survival rates in non-metastatic (M0) patients. Subjects and methods We included 4068 hypopharyngeal carcinoma patients identified from the Surveillance, Epidemiology, and End Results Program database between 2004 and 2015 in a retrospective cohort study. We analyzed prognostic factors of hypopharyngeal carcinoma using cause-specific and overall survival rates. We developed a nomogram to predict patients’ survival rates by multivariate Cox regression. Results Five-year survival rates of all stages between 2004 and 2010 were 25–35%. Radiotherapy pre- and post-surgery was the best modality of treatment according to 1-year and 5-year survival rates. The worst survival was in the posterior wall of the hypopharynx significantly (HRs [95% CI], P) (1.238 [1.045–1.466], P = 0.013). The highest survival rate was for the combination of surgery and radiotherapy pre- and post-surgery compared to radiation pre-surgery (0.532 [0.231–1.225], P = 0.138). Our nomogram revealed a better predictive probability over the 6th AJCC-TNM classification for predicting 5-year overall survival. Conclusions The worst survival was old age hypopharyngeal carcinoma patients, with the primary site in the posterior wall of the hypopharynx. The best survival was linked to receiving radiotherapy pre- and post-surgery. Our nomogram revealed a better predictive probability over TNM classification for predicting 1- and 5-year overall survival, which enables clinicians to make better treatment recommendations.
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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 | 0.008 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".