Reliability of staging, prognosis, and comorbidity data collection in the National Comprehensive Cancer Network (NCCN) non‐Hodgkin lymphoma (NHL) multicenter outcomes database
Bibliographic record
Abstract
BACKGROUND: Clinical trials and outcomes studies often rely on nonphysicians to abstract complex data from medical records, but the reliability of these data are rarely assessed. METHODS: We used standardized charts of patients with non-Hodgkin lymphoma to assess the reliability of key clinical data elements abstracted by 6 clinical research associates (CRAs), 3 project staff, and 3 medical oncologists. We assessed reliability on 5 variables: MD-reported and rater-determined disease stage; International Prognostic Index (IPI; low-low intermediate, intermediate-high, high); Charlson comorbidity index score; and presence of any item from the Charlson index. Intraclass correlation coefficients (ICCs) of 0-0.20 were indicative of "slight", 0.21-0.40 indicated "fair", 0.41-0.60 indicated "moderate", 0.61-0.80 "substantial" and >0.80 "almost perfect" reliability. RESULTS: By outcome, the ICC (95% confidence interval) values for MD-reported stage, rater-determined stage, and IPI were 0.86 (0.67, 0.94), 0.82 (0.59, 0.93), and 0.80 (0.55, 0.92), respectively. In contrast, the ICC (95% confidence interval) of the Charlson score, or presence of any Charlson comorbidity item was 0.47 (0.03, 0.75) and 0.61 (0.23, 0.83), respectively. Reliability varied by rater group; no rater group was consistently more reliable than others. CONCLUSIONS: Trained CRAs abstracted key clinical variables with a very high degree of reliability, and performed at a level similar to study trainers and oncologists. Elements of the Charlson index were less reliable than other data types, possibly because of inherent ambiguity in the index itself.
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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 | 0.102 | 0.193 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".