Accuracy of Fine Needle Aspiration (FNA) of the Thyroid in Identifying Papillary Carcinoma in Liquid-Based Cytology
Bibliographic record
Abstract
Background:The accuracy of FNA of the thyroid in predicting papillary carcinoma in large studies ranges from 94 to 98%.The unsatisfactory / non-diagnostic rate is usually around 5% this is based on studies performed mostly on conventional smears.We compare these studies with FNA of the thyroid using liquid-based (ThinprepTM) cytology. Methods:We reviewed cases of papillary carcinoma of the thyroid diagnosed by histology in our institution between January 2010 and December 2011.We only included cases that had previous FNA.We have used exclusively liquid-based cytology (ThinprepTM) since 1996.Cases of micro-carcinoma (<1 cm) were excluded from the study.Cases diagnosed by FNA as benign or follicular lesion were considered negative and those given the diagnosis of follicular neoplasm, suspicious, or papillary carcinoma were considered positive.Results: There were 149 cases of papillary carcinoma of the thyroid diagnosed by surgical resection and that had a previous FNA.FNAs were positive in 132 of the cases (88%).Three cases were unsatisfactory (2%) and 15 cases were negative (10%).Of the 15 negative cases 9 (60%) were called follicular lesion.Review of the negative cases showed difficulty in identifying nuclear abnormalities and papillary architecture.Colloid and its patterns were difficult to identify. Conclusion:The use of liquid-based cytology shows a higher yield of diagnostic material for the identification of papillary carcinoma of the thyroid with a lower incidence of non-diagnostic material.The false negative rate is however higher than reported in the literature that may be related to difficulty identifying nuclear abnormalities, background colloid and papillary architecture.
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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 | 0.019 | 0.084 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".