Current usefulness of aspiration cytology (FNAC) in the head and neck diagnosis.
Bibliographic record
Abstract
BACKGROUND: Fine Needle Aspiration Cytology (FNAC) is a well established and widely used method for both a preliminary and sometime final non-invasive pathologic diagnosis. FNAC is a simple and inexpensive diagnostic tool and should represent the standard of care in developing and resource-poor countries while maintaining its diagnostic usefulness in developed and advanced ones. METHODS: The concordance between preoperative FNAC and final histology was evaluated in 168 patients operated on at the Otorhinolaryngology Unit, "A. Murri" Hospital, Fermo (Italy), from January 2012 to October 2016, including thyroid cases, salivary glands and cervical masses. RESULTS: The percentages of correct diagnosis provided by FNAC were good in all groups of pathologies and in accordance with the mean data of the literature. In particular the kappa statistic for the degree of agreement between FNAC and definitive histology (good > 0.6 and excellent > 0.8) was 0.74 for the thyroid, 0.83 for the parotid and 0.71 for both the submandibular and the cervical masses. DISCUSSION: Thy 3 group is still the most challenging for a successful FNAC diagnostic prediction. Especially in the developed and advanced countries, both the immediate review of the smear with its repetition, if needed, and the aspiration performed under CT/MRI guidance, when necessary, seem to further empower FNAC diagnostic resolution and should be pursued. Being routinely used for more than 40 years, FNAC is still a valuable and cost-effective tool to distinguish between cases that don't need any treatment, cases to be treated medically and those that require surgical excision. In the Authors' opinion every institution should periodically review its data in order to monitor and assess the accuracy of its diagnostic activity.
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 | 0.019 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 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".