Effectiveness of fine‐needle aspiration cytology of breast: Analysis of 2,375 cases from northern Thailand
Bibliographic record
Abstract
At the Maharaj Nakorn Chiang Mai Hospital, Chiang Mai, Thailand, 2,375 cases of breast lesions were sampled by fine-needle aspiration (FNA) from 1994-1999. Cytologic diagnoses were: benign (48%), suspicious for malignancy (5%), malignant (15%), and unsatisfactory (32%). Comparison with histology was possible in 721 cases. The diagnoses obtained by FNA showed a sensitivity of 84.4%, specificity of 99.5%, positive predictive value of 99.8%, negative predictive value of 84.3%, false-negative rate of 16.7%, false-positive rate of 0.5%, and overall diagnostic accuracy of 91.3%. We conclude that, in experienced hands, FNA of breast masses is reliable for diagnosis. Assessment of samples at the time of aspiration can reduce the number of inadequate specimens to near zero. Correlation of FNA results with clinical and radiologic findings can identify false-negatives and false-positives, ensuring optimal patient management. Many centers now recommend needle core biopsy instead of FNA. For regions such as ours, the added cost of this test would make it unavailable to many patients, which could delay a diagnosis of breast cancer. We advocate keeping FNA as a first-line diagnostic procedure, at least in areas under economic restrictions, in order to maximize the availability of health care to women with breast disease.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".