A Practical Approach to Investigating Cross-Contaminants in the Anatomical Pathology Laboratory
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
Tissue contaminants in anatomical pathology are not uncommon. While issues related to the presence of extraneous tissue on glass slides are often easily resolved, this is not always the case and several factors may contribute to diagnostic difficulty. Because of this, familiarity with the different steps involved in handling specimens in the anatomical pathology laboratory is essential when troubleshooting possible cross-contaminants. Most commonly, the specimen constituting the source of cross-contamination is handled before the actual contaminated case; however, this is not always so. In this article, we review the steps involved in processing pathology specimens as they pertain to cross-contamination; share an approach covering how to troubleshoot and prevent tissue contaminants in a systematic and practical manner; present some examples from our own experiences; and compare our experience to what is reported in the literature. The information included in this article will be of use to all members of the anatomical pathology team including medical laboratory technologists, laboratory managers and supervisors, pathologist assistants, trainees in pathology, and pathologists.
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.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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