Utility of clinical features with fine needle aspiration biopsy for diagnosis of Warthin tumor
Bibliographic record
Abstract
BACKGROUND: Conservative management of Warthin tumor (WT) may be a viable alternative to surgery, but there are concerns of missed malignancies on fine needle aspiration biopsy (FNAB). The purpose of this study is to measure the sensitivity and positive predictive value of FNAB for WT, and to identify clinical features associated with WT that can aid in this diagnosis. METHODS: Retrospective analysis of patients from January 1, 2006 to April 30, 2017 at a tertiary care center in London, Ontario, Canada. All patients with a diagnosis of WT on FNAB or resection were included. Electronic medical records were identified for 177 patients that fit the criteria. Study outcomes included the sensitivity and positive predictive value of FNAB alone for WT, and, when including clinical features associated with WT. RESULTS: The mean age of patients in this study was 63.2 years (SD 10.4); 115 (65%) were male, and 157 (89%) were past or present smokers. The measured sensitivity and positive predictive value of FNAB for WT were 95.8 and 97.2% respectively. Two cases were classified as WT on FNAB but confirmed at resection as mucoepidermoid carcinoma and acinic cell carcinoma. When only patients with multifocal, bilateral or incidental tumors were assessed, sensitivities and positive predictive values for each were 100%. Isolating for inferior pole location also resulted in a positive predictive value of 100%. CONCLUSIONS: The sensitivity and positive predictive value of FNAB for WT in this study are high, with two false negatives on FNAB. Multifocal, bilateral, incidentaloma and inferior pole location were identified as potential clinical features that may increase the diagnostic confidence for WT, strengthening the argument for conservative management in these patients. Overall, this study serves as an initial exploration into whether clinical features may be included with FNAB results to improve the sensitivity and positive predictive value of diagnosing WT. Further research is necessary before these findings can be translated into clinical practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".