Immunocytochemistry for diagnostic cytopathology—A practical guide
Classification
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".
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
Cytological specimens, which are obtained by minimally invasive methods, are an excellent source of diagnostic material. Sometimes they are the only material available for diagnosis as well as for prognostic/predictive markers. When cytomorphology is not straightforward, ancillary tests may be required for a definitive diagnosis to guide clinical management. Immunocytochemistry (ICC) is the most common and practical ancillary tool used to reach a diagnosis when cytomorphology is equivocal, to differentiate entities with overlapping morphological features, and to determine the cell lineage and the site of origin of a metastatic neoplasm. Numerous immunomarkers are available, and some are expressed in multiple neoplasms. To rule out entities within a differential diagnosis, the use of more than one marker, sometimes panels, is necessary. ICC panels for diagnostic purposes should be customised based on the clinical context and cytomorphology, and the markers should be used judiciously to preserve material for additional tests for targeted therapies in the appropriate setting. This review offers a practical guide for the use of ICC for diagnostic cytopathology, covering the most commonly encountered non-hematolymphoid diagnostic scenarios in various body sites.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| 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