Preoperative Corticosteroids Reduce Diagnostic Accuracy of Stereotactic Biopsies in Primary Central Nervous System Lymphoma: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Despite general acceptance that corticosteroid therapy (CST) should be withheld before biopsy for suspected primary central nervous system lymphoma (PCNSL), there remains conflicting evidence surrounding the precise impact of preoperative CST on the histopathological diagnosis. The objective of this systematic review and meta-analysis was to describe and quantify the effects of preoperative CST on the diagnostic accuracy of biopsies for PCNSL. METHODS: Primary articles were screened from Ovid MEDLINE, Embase, Web of Science, and Scopus databases. Meta-analysis was performed for immunocompetent patients with histologically confirmed PCNSL. Subgroup and regression analyses were performed to assess the effects of biopsy type, CST duration, dose, and preoperative taper on the diagnostic accuracy. In addition, the sensitivity of cerebrospinal fluid (CSF) analyses for PCNSL was assessed. RESULTS: Nineteen studies, comprising 1226 patients (45% female; mean age: 60.3 years), were included. Preoperative CST increased the risk of nondiagnostic biopsy with a relative risk (RR) of 2.1 (95% CI: 1.1-4.1). In the stereotactic biopsy subgroup, the RR for nondiagnostic biopsy was 3.0 (95% CI: 1.2-7.5). CST taper, duration, and dose did not significantly influence diagnostic biopsy rates. The sensitivity of CSF cytology, including flow cytometry, for PCNSL was 8.0% (95% CI: 6.0%-10.7%). CONCLUSION: Our results suggest that preoperative CST reduces the diagnostic yield of stereotactic biopsies for PCNSL. We found no evidence that tapering CST before biopsy improves diagnostic rates. CSF analysis currently has a poor sensitivity for the diagnosis of PCNSL.
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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 | 0.010 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.031 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".