Retrospective study on the correlation between MASCC score and the evolution of febrile neutropenia for patients with solid cancer.
Bibliographic record
Abstract
e13570 Retrospective study on the correlation between MASCC score and the evolution of febrile neutropenia for patients with solid cancer. Background: The Multinational Association for Supportive Care in Cancer (MASCC) score is used to risk-stratify outpatients with febrile neutropenia. Currently, data on the use of the MASCC score are based on studies with a small number of patients. Our primary aim was to determine whether a MASCC score ≥ 21 identifies patients with solid tumors who would evolve without complications. Methods: We conducted a retrospective cohort study of patients admitted with febrile neutropenia and solid cancer at Sherbrooke University Hospital from 2011 to 2022. We collected patients' demographics, type of cancer, current chemotherapy regimen, whether patients were candidates for outpatient treatment, MASCC score, duration of IV antibiotic therapy, complications (intensive care unit admission, hypotension, hypoxemia, acute kidney failure, bacteriemia (persistent or developing after 48h of IV antibiotics) and escalation in antimicrobial regimen after hospital admission), and inpatient deaths. We used chi-square analysis and multivariate analysis to determine factors other than MASCC that predict complications. Results: A total of 290 febrile neutropenia patients with solid tumors were identified. 93 patients had a MASCC score <21 and 196 were identified as low risk (MASCC score ≥ 21). The preliminary results showed that the MASCC score had a specificity of 58,3% [95% CI 48,4%-67,8%], a sensibility of 83,0% [95% CI 76,7%-88,1%], a positive predictive value of 77,0% [95% CI 72,7%-80,9%] and a negative predictive value of 67,0 [95% CI 58,7%-74,4%] to identify complications. A low risk MASCC score showed statistically significative reduction in duration of neutropenia, IV antibiotics, fever and length of hospitalisation. Conclusions: The MASCC score did not demonstrate high enough accuracy to precisely identify patients which would have evolved without complications. However, a low-risk score identified by using the MASCC score was found to have strong association with reduction of duration of hospitalisation burden. Furter data will be collected to increase the strength of the study.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 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".