Imprint cytology: Invaluable technique to evaluate fresh specimens received in the pathology department for lymphoma workup
Bibliographic record
Abstract
At the time of intraoperative consultation, cytologic preparations including smears and imprints can be used in combination with frozen sections to increase diagnostic yield; however, these simple and rapid techniques are not adopted by all pathologists and their use varies considerably between institutions. In patients under investigation for suspected lymphoma, optimal triaging of tissue received fresh in pathology for lymphoma workup is paramount to maximize the odds of obtaining an accurate and clinically meaningful diagnosis and to avoid the need for additional procedures and delays in management, particularly in the current context in which core biopsies have become common practice as a first attempt to attain this goal. Imprint cytology is invaluable in this regard, also as these patients may not have a lymphoma but rather one of its clinical mimics. Herein, imprint cytology is used to approach fresh specimens received intraoperatively for lymphoma workup. More specifically, how these specimens are triaged for ancillary studies, such as flow cytometry, florescence in situ hybridization, or molecular analyses based on an interpretation of the touch imprints, is described. Detailed imprint cytological findings of typical benign and malignant lymphoid and nonlymphoid lesions are discussed and illustrated.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".