Computerized tomography based tumor-thickness measurement is useful to predict postoperative pathological tumor thickness in oral tongue squamous cell carcinoma
Bibliographic record
Abstract
BACKGROUND: Tumor thickness has been shown in oral tongue squamous cell carcinoma (OTSCC) to be a predictor of cervical metastasis. The postoperative histological measurement is certainly the most accurate, but it would be of clinical interest to gain this information prior to treatment planning. This retrospective study aimed to compare the tumor thickness measurement between preoperative, CT scan, and surgical specimens . METHODS: We retrospectively included 116 OTSCC patients between 2001 and 2013. Thickness was measured on computer tomography imaging and again surgical specimens. RESULTS: The median age was 66 years. 62.8 % of patients were smokers with a mean of 31.4 pack-years. Positive nodal disease was reported in 41.2 %. Mean follow-up time was 33.1 months. The correlation between CT scan-based tumor thickness and surgical specimens based thickness was significant (Spearman rho = 0.755, P < 0.001). CONCLUSION: Tumor thickness assessed by CT scan may provide an accurate estimation of true thickness and can be used in treatment planning.
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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 | 0.004 |
| 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".