Immunocytochemistry as an adjunct to diagnostic cytology
Bibliographic record
Abstract
Immunocytochemistry as a routine ancillary test remains a distant reality for most diagnostic laboratories. Notable barriers to the mass deployment of ICC include: the large variety of specimen preparations, the small specimen size, lack of validation and lack of control specimens. As clinicians constantly strive to answer questions relating to diagnosis, therapy and prognosis with minimally invasive sampling techniques, the cytopathology community must endeavour to adopt ancillary specimen testing by ICC as a core element of diagnostic cytology. L’immunocytochimie est une technique complémentaire qui devrait être de routine ; cela reste néanmoins loin de la réalité pour de nombreuses structures d’anatomie et cytologie pathologiques ; les raisons en sont multiples : diversité des techniques de préparation cytologique parfois au sein d’une même structure ; manque de reproductibilité du matériel cellulaire ou pauvreté cellulaire ; absence de validation de certaines techniques en cytologie ; absence de témoins, etc. Néanmoins, la cytopathologie ne devrait plus, de nos jours, se concevoir sans l’aide de l’immunocytochimie qui apporte, comme en histologie, fiabilité diagnostique et éléments pronostiques.
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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 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".