Presenting international prognostic scoring system affects time to treatment: A retrospective single center review.
Bibliographic record
Abstract
e20058 Background: Diffuse large B cell lymphoma (DLBCL) is an aggressive form of non-Hodgkin’s lymphoma, which requires prompt initiation of chemotherapy to cure, as any delays can result in worse outcomes. A recent study in Japan found that patients with an IPI score ≥3 had a worse prognosis if their diagnosis was delayed, whereas with an IPI score < 3, there was no effect. Methods: This study investigated how long Windsor patients wait to receive chemotherapy after diagnosis, and how this affects relapse status. A retrospective chart review was conducted, looking at all patients diagnosed with DLBCL who underwent treatment at the Windsor Regional Cancer Center from 2007 to 2018 (N = 317). Each chart was reviewed for variables relating to treatment and outcomes. Results: Overall survival within the studied time period was 71% with the median survival time of 11.99 years. The overall relapse rate was 19.24% with a mean relapse-free interval of 4.87 years. A longer time to initial treatment resulted in a decreased risk of relapse (p = 0.038); however, these patients tended to have a lower stage (p = 0.002), no family history of cancer (p < 0.001), and lower IPI scores (p = 0.004). When broken down by IPI score, the mean times to treatment after diagnosis were: IPI 0 = 40 days; IPI 1 = 35 days; IPI 2 = 38 days; IPI 3 = 29 days; IPI 4 = 22 days; IPI 5 = 29 days. Conclusions: These results show that while DLBCL patients with worse prognostic measures are prioritized to receive treatment quicker, there is a large discrepancy in time to treatment between IPI scores. There was an 11-day delay between early and late IPI scores, likely driven by patient symptoms, rather than evidence of safety. The natural history of DLBCL is known to be aggressive, but more granular information is necessary to determine appropriate wait times for presenting IPI score. Further research should focus on identifying delays in the process, and if it can be streamlined to improve wait times for all patients, regardless of IPI score.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".