Preanalytic specimen triage: Smears, cell blocks, cytospin preparations, transport media, and cytobanking
Bibliographic record
Abstract
With increasing requests for the evaluation of prognostic and predictive molecular biomarkers, great attention must be paid to the preanalytical issues regarding sample quality and DNA/RNA yield from all different types of cytological preparations. The objectives of this review were: 1) to provide an update regarding the importance of specimen triage as well as specimen handling and collection; 2) to discuss the different cell preparations that can be used for molecular testing, their advantages and limitations; and 3) to highlight the strategies for biobanking cytology samples. Good-quality DNA/RNA can be harvested from fresh cells in cell suspensions, formalin-fixed paraffin-embedded cell blocks, archival stained smears, archival unstained cytospin preparations, liquid-based cytology slides, FTA cards, and cryopreserved cells. In contrast to formalin-fixed paraffin-embedded tissue specimens (small biopsies and surgical resections), the multitude of types of sample preparations as well as the diversity in sample collection and processing procedures make cytology an ideal specimen for most genomic platforms, with less DNA and RNA degradation and a purer sample, usually with a higher concentration of tumor cells. The broad incorporation of cytological specimens into clinical practice. A should increase the number of samples potentially available for molecular tests and avoid repeat invasive procedures for tissue procurement, thereby increasing patient safety. In this context, it is of utmost importance that cytopathologists become familiar with the variables that can affect test results and embrace the goal of excellence in sample quality. Cancer Cytopathol 2017;125(6 suppl):455-64. © 2017 American Cancer Society.
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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.007 |
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".