Prevalence of occult nodal metastases in squamous cell carcinoma of the temporal bone: a systematic review and meta-analysis
Bibliographic record
Abstract
PURPOSE: Primary: To determine the rate of occult cervical metastases in primary temporal bone squamous cell carcinomas (TBSSC). Secondary: to perform a subgroup meta-analysis of the risk of occult metastases based on the clinical stage of the tumour and its risk based on corresponding levels of the neck. METHODS: A systematic review and meta-analysis of papers searched through Medline, Cochrane, Embase, Scopus and Web of Science up to November 2021 to determine the pooled rate of occult lymph node/parotid metastases. Quality assessment of the included studies was assessed through the Newcastle-Ottawa scale. RESULTS: Overall, 13 out of 3301 screened studies met the inclusion criteria, for a total of 1120 patients of which 550 had TBSCC. Out of the 267 patients who underwent a neck dissection, 33 had positive lymph nodes giving a pooled rate of occult metastases of 14% (95% CI 10-19%). Occult metastases rate varied according to Modified Pittsburg staging system, being 0% (0-16%) among 12 pT1, 7% (2-20%) among 43 pT2 cases, 21% (11-38%) among 45 pT3, and 18% (11-27%) among 102 pT4 cases. Data available showed that most of the positive nodes were in Level II. CONCLUSION: The rate of occult cervical metastases in TBSCC increases with pathological T category with majority of nodal disease found in level II of the neck.
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.035 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".