Risks of malignancy in the major nongynecologic cytopathology reporting systems: Critiques and discussions
Bibliographic record
Abstract
The ever-increasing popularity of standardized systems for reporting cytopathology has led in part to much attention to and importance of the risk stratification schemes, especially the risks of malignancy (ROMs), which are associated with the different diagnostic categories and upon which recommendations for clinical management are based. However, it is well known that the ROM calculations are based on retrospective reviews of the existing literature, representing a heterogeneous patient population, and are plagued by significant biases and variations. Statistically, the ROM represents the post-test probability of malignancy, which changes with the test result and with the prevalence of malignancy (or pretest probability) in an individual practice setting and individual patient presentation. Therefore, the clinical utility of the ROM is questioned and likely needs a second look in the nongynecologic cytopathology reporting systems. In this communication, the authors discuss the status of the ROM estimates according to the most commonly used nongynecologic reporting systems, including for thyroid, salivary glands, and others, highlighting similarities and differences with a focus on the limitations of ROM estimates and their application in clinical practice.
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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 | 0.069 | 0.139 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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".