Next Generation Quality: Assessing the Physician in Clinical History Completeness and Diagnostic Interpretations Using Funnel Plots and Normalized Deviations Plots in 3,854 Prostate Biopsies
Bibliographic record
Abstract
BACKGROUND: Observational data and funnel plots are routinely used outside of pathology to understand trends and improve performance. OBJECTIVE: Extract diagnostic rate (DR) information from free text surgical pathology reports with synoptic elements and assess whether inter-rater variation and clinical history completeness information useful for continuous quality improvement (CQI) can be obtained. METHODS: All in-house prostate biopsies in a 6-year period at two large teaching hospitals were extracted and then diagnostically categorized using string matching, fuzzy string matching, and hierarchical pruning. DRs were then stratified by the submitting physicians and pathologists. Funnel plots were created to assess for diagnostic bias. RESULTS: 3,854 prostate biopsies were found and all could be diagnostically classified. Two audits involving the review of 700 reports and a comparison of the synoptic elements with the free text interpretations suggest a categorization error rate of <1%. Twenty-seven pathologists each read >40 cases and together assessed 3,690 biopsies. There was considerable inter-rater variability and a trend toward more World Health Organization/International Society of Urologic Pathology Grade 1 cancers in older pathologists. Normalized deviations plots, constructed using the median DR, and standard error can elucidate associated over- and under-calls for an individual pathologist in relation to their practice group. Clinical history completeness by submitting medical doctor varied significantly (100% to 22%). CONCLUSION: Free text data analyses have some limitations; however, they could be used for data-driven CQI in anatomical pathology, and could lead to the next generation in quality of care.
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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 | 0.167 | 0.380 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.013 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| 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".