The negative predictive value of ultrasound-guided 14-gauge core needle biopsy of breast masses: a validation study of 339 cases
Bibliographic record
Abstract
PURPOSE: To determine the negative predictive value of sonographically guided 14-gauge core needle biopsy of breast masses, with detailed analysis of any false-negative cases. MATERIALS AND METHODS: We reviewed 669 cases of sonographically guided 14-gauge core needle biopsies that had benign pathologic findings. Given a benign pathology on core biopsy, true-negatives had either benign pathology on surgical excision or at least 2 years of stable imaging and/or clinical follow-up; false-negatives had malignant histology on surgical excision. RESULTS: Follow-up was available for 339 breast lesions; 117 were confirmed to be benign via surgical excision, and 220 were stable after 2 years or more of imaging or clinical follow-up (mean follow-up time 33.1 months, range 24-64 months). The negative predictive value was determined to be 99.4%. There were 2 false-negative cases, giving a false-negative rate of 0.1%. There was no delay in diagnosis in either case because the radiologist noted discordance between imaging and core biopsy pathology, and recommended surgical excision despite the benign core biopsy pathology. CONCLUSIONS: Sonographically guided 14-gauge core needle biopsy provides a high negative predictive value in assessing breast lesions. Radiologic/pathologic correlation should be performed to avoid delay in the diagnosis of carcinoma.
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 | 0.017 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".