Factors predicting poor outcomes in <scp>T1N0</scp> oral squamous cell carcinoma: indicators for treatment intensification
Bibliographic record
Abstract
BACKGROUND: This study investigated the impact of adverse pathological features (APFs) amongst patients with T1N0 oral squamous cell carcinoma (OSCC) on both tumour control and survival. We aimed to investigate clinicopathological factors that would predict poor outcomes and determine a clinically relevant threshold for the recommendation of additional treatment. METHODS: Retrospective analysis of 121 patients from a single institution (1988-2013) who were treated with surgery only (wide local excision of the primary tumour with or without neck dissection). Only patients who are pT1cN0 or pT1pN0 were included. Patients who had received adjuvant radiotherapy were excluded from the study. RESULTS: APFs were associated with increased regional failure included tumour thickness (TT) ≥5 mm (P = 0.007), perineural invasion (PNI) (P = 0.003), infiltrative border (P = 0.030) and poor differentiation (P = 0.005). Poorly differentiated tumours were also associated with increased local failure (P = 0.03). Local control (LC), regional control (RC) and disease-specific survival (DSS) decreased with an increasing number of APFs (P = 0.009, P = <0.001 and P = 0.009, respectively). Patients with four or more APFs had significantly worse outcomes in LC (P < 0.001), RC (P < 0.001) and DSS (P < 0.001). CONCLUSION: T1N0 OSCC exhibiting four or more APFs or demonstrating poor differentiation on histology had an increased risk of locoregional failure. The presence of PNI, infiltrative border and TT ≥5 mm are associated with increased regional failure. These factors may prompt escalation of treatment for patients with T1N0 OSCC.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".