Timeliness and quality of diagnostic care for medicare recipients with chronic lymphocytic leukemia
Bibliographic record
Abstract
BACKGROUND: Little is known about the patterns of care relating to the diagnosis of chronic lymphocytic leukemia (CLL), including the use of modern diagnostic techniques such as flow cytometry. METHODS: The authors used the SEER-Medicare database to identify subjects diagnosed with CLL from 1992 to 2002 and defined diagnostic delay as present when the number of days between the first claim for a CLL-associated sign or symptom and SEER diagnosis date met or exceeded the median for the sample. The authors then used logistic regression to estimate the likelihood of delay and Cox regression to examine survival. RESULTS: For the 5086 patients analyzed, the median time between sign or symptom and CLL diagnosis was 63 days (interquartile range [IQR] = 0-251). Predictors of delay included age ≥75 (OR 1.45 [1.27-1.65]), female gender (OR 1.22 [1.07-1.39]), urban residence (OR 1.46 [1.19 to 1.79]), ≥1 comorbidities (OR 2.83 [2.45-3.28]) and care in a teaching hospital (OR 1.20 [1.05-1.38]). Delayed diagnosis was not associated with survival (HR 1.11 [0.99-1.25]), but receipt of flow cytometry within thirty days before or after diagnosis was (HR 0.84 [0.76-0.91]). CONCLUSIONS: Sociodemographic characteristics affect diagnostic delay for CLL, although delay does not seem to impact mortality. In contrast, receipt of flow cytometry near the time of diagnosis is associated with improved survival.
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".