Accuracy of bronchial brush and wash specimens prepared by the ThinPrep method in the diagnosis of pulmonary small cell carcinoma
Bibliographic record
Abstract
BACKGROUND: ThinPrep bronchial brush and wash accuracy in the diagnosis of pulmonary small cell carcinoma (pSCCa) and measured as sensitivity, specificity, positive and negative predictive values (PPV and NPV) is incompletely studied or unknown. METHODS: Specimens collected over 5 years from 199 pSCCa and 938 negative (Neg) for pulmonary cancer individuals were selected by linking the laboratory file with the cancer registry. Results other than unsatisfactory were classified as true-positive and -negative, and false-positive and -negative tests so as to calculate accuracy estimates. Slides of all false-negative and -positive and randomly selected samples of true-positive and -negative tests were evaluated for 11 abnormal cell features typical of pSCCa in conventional preparations: distribution differences by disease status were tested for significance. RESULTS: There were 129 brush and 170 wash in the pSCCa group and 365 brush and 1153 wash in the Neg group. Of all specimens, 1.2% were unsatisfactory. Brush sensitivity, specificity, PPV, and NPV were 61.9%, 99.4%, 97.5%, and 88%, respectively. Wash frequencies were 53.3%, 98.8%, 86.5%, and 93.5%, respectively. Abnormal cell features occurred in 29.9% of the selected pSCCa and 4.7% of the Neg specimens, and distribution differences were significant for each feature (P < .001). CONCLUSIONS: Unsatisfactory brush and wash specimens are infrequent in the diagnosis of pSCCa, and both have moderate sensitivity and high specificity, PPV, and NPV. pSCCa abnormal cell features resemble those seen in conventional preparations and can distinguish specimens with pSCCa from those negative for pulmonary cancer.
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 | 0.001 | 0.000 |
| 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".