Respiratory cytology: Differential diagnosis and pitfalls
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Pulmonary cytology can be challenging and has its share of diagnostic pitfalls. Reactive atypia can occasionally be alarming, leading to diagnostic pitfall for a false-positive diagnosis of malignancy, even for experienced cytopathologists (Naryshkin and Young, Diagn Cytopathol 1993;9:89-97). In addition, cytologic preparations can show an absence of architectural clues, leading to diagnostic difficulties. Some conditions can cytologically as well as clinically and radiographically mimic malignancies, making these pitfalls even more frequent (Bedrossian et al., Lab Med 1983;14:86-95). A recent report stated that "no laboratory that aims to make definitive diagnoses in pulmonary cytology can be spared from false-positive results"(Policarpio-Nicolas and Wick, Diagn Cytopathol 2008;36:13-19). A false-positive finding could produce unnecessary treatment and morbidity, whereas false-negative diagnosis could result in delayed diagnosis and treatment. This review analyzes and illustrates cellular changes and benign entities that can mimic malignancy in respiratory cytology as well as neoplasms that could lead to a false-negative diagnosis. In addition, some specific challenging and difficult aspects in classification of pulmonary malignancies will be discussed. Guidelines and clues are presented to avoid such pitfalls.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it