Predictive Factors for Diagnosis of Advanced-Stage Squamous Cell Carcinoma of the Head and Neck
Bibliographic record
Abstract
OBJECTIVE: To identify the predictive factors (with emphasis on diagnostic delay) associated with the diagnosis of an advanced-clinical stage head and neck cancer. DESIGN: Cross-sectional study of patients with head and neck cancer originally recruited for a case-control study. SETTING: Three referral oncological centers in metropolitan areas in southern Brazil: São Paulo, Curitiba, and Goiânia. PATIENTS: The study population comprised 679 patients recently diagnosed as having a previously untreated head and neck squamous cell carcinoma. MAIN OUTCOME MEASURE: Diagnosis of advanced disease (clinical stage III-IV) head and neck cancer. RESULTS: Patients with laryngeal and hypopharyngeal cancers were more likely to be diagnosed as having advanced disease than those with lip, oral, and oropharyngeal cancers (88.0% vs 74.6%) (P<.001). Patient delay was inversely associated with clinical stage at diagnosis in patients with the same cancers, while professional delay was directly associated with a higher risk of advanced clinical stage at diagnosis (P =.001 and P =.006, respectively). In the analysis of laryngeal and hypopharyngeal cancer, both patient and professional delays were associated with advanced disease, with patient delay being a stronger predictive factor than professional delay. CONCLUSIONS: Clinical stage at diagnosis was associated with sociodemographic characteristics, patient delay, and professional delay. Our results indicate that continued educational programs for the population and health care professionals regarding the identification of early symptoms of head and neck cancers are warranted.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".