A different entity: A population based study of characteristics and recurrence patterns in oropharyngeal squamous cell carcinomas
Bibliographic record
Abstract
BACKGROUND: Cases of squamous cell carcinoma (SCC) of the oropharynx were compared with other head and neck cancer (HNC) anatomic subsites in patients treated at the provincial referral centre for HNC, the Nova Scotia Cancer Centre (NSCC). METHODS: A retrospective chart review was performed on HNC patients assessed at the NSCC between 2010 and 2011. Patient demographics, disease characteristics, treatment details and outcomes, including recurrence rates and survival were collected. Data was collected on new and recurrent cases of HNC. This data was compared between the two types of HNC using chi-square tests for dichotomous categorical variables or Fishers exact test where appropriate. Wald test was used to compare categorical variables with 3 categories. Continuous variables were compared using the non-parametric Wilcoxon test. RESULTS: 318 charts were included in the analysis. 122 (38%) were oropharyngeal squamous cell carcinomas (OPSCCs). In terms of disease characteristics, OPSCCs were more likely to be poorly differentiated/undifferentiated (n = 267, 49(40%) vs 42(21%), p < 0.001), non-keratinizing (n = 169, 25(20%) vs 17(9%), p < 0.001), greater than 2 cm (n = 253, 72(59%) vs 78(40%), p = 0.0061), stage 4 (n = 313, 55(45%) vs 64(33%), p = 0.0315) and have had locoregional nodal spread (n = 315, 103(84%) vs 55(28%), p < 0.001). In the subset of 57 patients that had p16 testing, OPSCCs were more likely to be p16(+) (37(30%) vs 1(1%), p < .001). There were no significant differences in terms of Charlson probability of 10 year survival, smoking or alcohol consumption although OPSCC patients were significantly less likely to have COPD as a co-morbidity (n = 318, 19(16%) vs 53(27%), p = 0.0175). Finally, OPSCCs had less chance for relapse than non-OPSCCs in both univariate (2.119 times less, p=0.0034) and multivariate (1.899 times less, p=0.0505) analyses along with a 1.822 times less overall mortality in a multivariae analysis (p=0.0408). CONCLUSIONS: This analysis suggests that Nova Scotian OPSCCs should be considered distinct from other HNC lesions, most notably in terms of disease characteristics and prognosis. Specifically, despite a higher association with disease factors traditionally considered to be linked to poor prognosis, outcomes were actually superior in terms of relapse and overall mortality.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".