Identification of Tissue Contamination by Polymorphic Deletion Probe Fluorescence In Situ Hybridization
Bibliographic record
Abstract
Potential sources of error in surgical pathology include specimen misidentification, unidentified tissue, and tissue contamination of paraffin blocks and slides. Current molecular approaches to characterize unidentified or misidentified tissue include fluorescence in situ hybridization identification of sex chromosomes (XY FISH) and microsatellite analysis. Polymorphic deletion probe (PDP) FISH, a novel FISH assay based on copy number variants, can distinguish between cells and tissues from 2 individuals in situ, independent of gender. Using a panel of 3 PDPs, we compared the genotypes of potential tissue contaminants (n=19) and unidentified tissues (n=6) with patient tissues to determine the utility of PDP FISH in resolving specimen identity. XY FISH was added to increase the informative potential of the assay, and microsatellite analysis was used as a gold standard to confirm PDP FISH results. PDP FISH distinguished between putative contaminants and patient tissues in 13 of 14 cases and indicated a high likelihood of 2 tissues originating from the same source in 11 of 11 cases. The assay has a sensitivity and specificity of 86% [6/7, exact 95% confidence interval (CI): 42%, 97%] and 100% (9/9, exact 1-sided 97.5% CI: 68%, 100%), respectively, and a positive predictive value and negative predictive value of 100% (6/6, exact 1-sided 97.5% CI: 54%, 100%) and 90% (9/10, exact 95% CI: 55%, 98%), respectively. PDP FISH is an accurate and practical molecular assay for the genetic characterization of potential tissue contaminants and unidentified tissues, especially in the setting of small sample size, and permits concomitant assessment of morphology.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 0.001 |
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".