Reliability of self-report versus chart-based prostate cancer, PSA, DRE and urinary symptoms.
Bibliographic record
Abstract
INTRODUCTION: Medical chart-review and self-reported questionnaire are two common methods of determining cancer screening and symptoms. We investigate the validity of these methods and therefore of a class of clinical/epidemiological studies. We compare variables on prostate cancer, any prostate-specific antigen (PSA) test, asymptomatic screening PSA, any digital rectal exam (DRE), and urinary symptoms. We used data from a 2005 case control study of PSA and metastatic prostate cancer (253 cases and 496 controls). Data were collected from 1999 to 2002. METHODS: We calculated kappa, percent agreement (PPA) and prevalence adjusted bias adjusted kappa (PABAK). We compared percentage positive response (PPR) and sensitivities/specificities of questionnaire against chart and vice versa. We measured the degree of differential agreement between cases and controls using odds ratios. RESULTS: We found almost perfect agreement on prostate cancer, moderate agreement on any PSA and DRE, and slight agreement on asymptomatic screening PSA and urinary symptoms. PABAK ranged from 0.134 (urinary symptoms) to 0.879 (prostate cancer). Differences between cases/controls in PPR are similar according to chart or questionnaire, though PPR itself is usually higher on the questionnaire. Only for any PSA (including diagnostic), cases had better recall than controls. We found no evidence of differential agreement that might lead to bias in a case control study. CONCLUSIONS: Some variables are more reliable than others comparing medical chart review and self-report. Diagnosis of prostate cancer has near perfect agreement, but for less catastrophic events such as PSA (especially asymptomatic screening tests), DRE or urinary symptoms, agreement ranges from slight to moderate.
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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 | 0.021 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".