Impact of Provider Volume on Outcomes of Patients With Hodgkin Lymphoma
Bibliographic record
Abstract
BACKGROUND: While the provider volume-outcome relationship has been established for many complex surgeries and invasive procedures, the provider volume impact on outcomes for Hodgkin lymphoma (HL) is less certain. We hypothesized that high-volume providers (HVPs) may have superior outcomes compared with low-volume providers (LVPs). METHODS: We performed a chart-based, retrospective review of all patients receiving adriamycin, doxorubicin, bleomycin, vinblastine, and dacarbazine (ABVD) for HL at the West Cancer Center from January 2010 to June 2015. Patients were divided into HVP (> 3 inpatient chemotherapy (CT)/month (m)) versus LVP (< 3 CT per m) groups. Of 95 patients identified, 93 received at least one dose of ABVD, 21 treated by HVP and 72 by LVP. Patient characteristics were well balanced between groups. RESULTS: HVPs were less likely to prescribe dose delays (odds ratio (OR): 0.32; confidence interval (CI): 0.16 - 0.65; P = 0.0007) and to hold doses for afebrile neutropenia (OR: 0.05; CI: 0.00 - 0.85; P = 0.0006). HVP delivered significantly fewer prophylactic growth factors (0% of doses vs. 42%, OR: 0.00; CI < 0.00 - 0.06; P < 0.0001). Both event-free survival (EFS) (HR: 6.68; CI: 1.10 - 7.63; P = 0.0321) and overall survival (OS) (HR: 3.68; CI: 1.11 - 12.22; P = 0.032) were significantly inferior in the patients treated by LVP. CONCLUSIONS: In this study, patients with HL treated by LVP had inferior outcomes compared with those treated by HVP. HVPs were less likely to prescribe dose delays, hold doses for afebrile neutropenia or administer growth factor prophylaxis. These observations need to be confirmed in alternative datasets.
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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 | 0.001 | 0.009 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".