The influence of the “cancer” label on perceptions and management decisions for low-grade prostate cancer
Bibliographic record
Abstract
BACKGROUND: Grade Group 1 (GG1) prostate cancer should be managed with active surveillance (AS). Global uptake of AS remains disappointingly slow and heterogeneous. Removal of cancer labels has been proposed to reduce GG1 overtreatment. We sought to determine the impact of GG1 disease terminology on individual's perceptions and decision making. METHODS: Discrete choice experiments were conducted on 3 cohorts: healthy men, canonical partners (partners), and patients with GG1 (patients). Participants reported preferences in a series of vignettes with 2 scenarios each, permuting key opinion leader-endorsed descriptors: biopsy (adenocarcinoma, acinar neoplasm, prostatic acinar neoplasm of low malignant potential [PAN-LMP], prostatic acinar neoplasm of uncertain malignant potential), disease (cancer, neoplasm, tumor, growth), management decision (treatment, AS), and recurrence risk (6%, 3%, 1%, <1%). Influence on scenario selection were estimated by conditional logit models and marginal rates of substitution. Two additional validation vignettes with scenarios portraying identical descriptors except the management options were embedded into the discrete choice experiments. RESULTS: Across cohorts (194 healthy men, 159 partners, and 159 patients), noncancer labels PAN-LMP or prostatic acinar neoplasm of uncertain malignant potential and neoplasm, tumor, or growth were favored over adenocarcinoma and cancer (P < .01), respectively. Switching adenocarcinoma and cancer labels to PAN-LMP and growth, respectively, increased AS choice by up to 17%: healthy men (15%, 95% confidence interval [CI] = 10% to 20%, from 76% to 91%, P < .001), partners (17%, 95% CI = 12% to 24%, from 65% to 82%, P < .001), and patients (7%, 95% CI = 4% to 12%, from 75% to 82%, P = .063). The main limitation is the theoretical nature of questions perhaps leading to less realistic choices. CONCLUSIONS: "Cancer" labels negatively affect perceptions and decision making regarding GG1. Relabeling (ie, avoiding word "cancer") increases proclivity for AS and would likely improve public health.
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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 | 0.007 | 0.032 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".