Molecular Auditing: An Evaluation of Unsuspected Tissue Specimen Misidentification
Bibliographic record
Abstract
CONTEXT.—: Specimen misidentification is the most significant error in laboratory medicine, potentially accounting for hundreds of millions of dollars in extra health care expenses and significant morbidity in patient populations in the United States alone. New technology allows the unequivocal documentation of specimen misidentification or contamination; however, the value of this technology currently depends on suspicion of the specimen integrity by a pathologist or other health care worker. OBJECTIVE.—: To test the hypothesis that there is a detectable incidence of unsuspected tissue specimen misidentification among cases submitted for routine surgical pathology examination. DESIGN.—: To test this hypothesis, we selected specimen pairs that were obtained at different times and/or different hospitals from the same patient, and compared their genotypes using standardized microsatellite markers used commonly for forensic human DNA comparison in order to identify unsuspected mismatches between the specimen pairs as a trial of "molecular auditing." We preferentially selected gastrointestinal, prostate, and skin biopsies because we estimated that these types of specimens had the greatest potential for misidentification. RESULTS.—: Of 972 specimen pairs, 1 showed an unexpected discordant genotype profile, indicating that 1 of the 2 specimens was misidentified. To date, we are unable to identify the etiology of the discordance. CONCLUSIONS.—: These results demonstrate that, indeed, there is a low level of unsuspected tissue specimen misidentification, even in an environment with careful adherence to stringent quality assurance practices. This study demonstrates that molecular auditing of random, routine biopsy specimens can identify occult misidentified specimens, and may function as a useful quality indicator.
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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 | 0.015 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".