Cytological preparations for molecular analysis: A review of technical procedures, advantages and limitations for referring samples for testing
Bibliographic record
Abstract
Minimally invasive procedures such as endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) must yield not only good quality and quantity of material for morphological assessment, but also an adequate sample for analysis of molecular markers to guide patients to appropriate targeted therapies. In this context, cytopathologists worldwide should be familiar with minimum requirements for refereeing cytological samples for testing. The present manuscript is a review with comprehensive description of the content of the workshop entitled Cytological preparations for molecular analysis: pre-analytical issues for EBUS TBNA, presented at the 40th European Congress of Cytopathology in Liverpool, UK. The present review emphasises the advantages and limitations of different types of cytology substrates used for molecular analysis such as archival smears, liquid-based preparations, archival cytospin preparations and FTA (Flinders Technology Associates) cards, as well as their technical requirements/features. These various types of cytological specimens can be successfully used for an extensive array of molecular studies, but the quality and quantity of extracted nucleic acids rely directly on adequate pre-analytical assessment of those samples. In this setting, cytopathologists must not only be familiar with the different types of specimens and associated technical procedures, but also correctly handle the material provided by minimally invasive procedures, ensuring that there is sufficient amount of material for a precise diagnosis and correct management of the patient through personalised care.
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 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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".