Preferred labels and language to discuss low-risk lesions that may be cancer precursors: A review
Bibliographic record
Abstract
OBJECTIVES: Patients diagnosed with low-risk lesions are confused about whether they have cancer, and experience similar anxiety to those with invasive cancer, which affects quality of life. Current labels for low-risk lesions were chosen by clinicians and lack meaning to patients. METHODS: We reviewed published research on preferred labels and language for low-risk lesions, and the rationale for those preferences. RESULTS: Of 6569 titles screened, we included 13 studies. Among healthy adults with cervix or prostate lesions, use of the term "cancer" rather than "nodule" or "lesion" resulted in greater anxiety, higher perceived disease severity, and selection of more invasive treatment. Physicians asked about removing "carcinoma" from thyroid lesion labels to reduce patient anxiety and discourage over-treatment did not support this change, instead preferring a term that included "neoplasm". CONCLUSIONS: This review revealed a startling paucity of research on preferences for low-risk lesion labels and language, and associated rationale. Future research is needed to understand how to improve communication about low-risk lesions. PRACTICE IMPLICATIONS: To reduce anxiety and improve the overall well-being of patients, it is crucial to gain a deeper understanding of how to improve patient-provider conversations regarding screen-detected lesions with a low risk of developing into invasive cancer.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".