Misdiagnosis and underdiagnosis of glioma: Systematic review
Bibliographic record
Abstract
Abstract Background Diagnostic errors in gliomas, a major group of brain tumors originating from glial cells, can have severe consequences for patients and healthcare systems. Despite the serious implications of a glioma diagnosis, there is a lack systematic evidence on the frequency of diagnostic errors. This study aimed to assess the prevalence of misdiagnosis and underdiagnosis of gliomas and their potential impact. Methods We conducted a systematic review of English-language original studies, including those with more than 10 participants, reporting diagnostic errors in gliomas. A search of Medline and Embase was performed from inception until October 28, 2024. We evaluated the proportions of diagnostic errors and their potential consequences, and assessed the risk of bias using a modified Newcastle-Ottawa Scale. Results Of 1,860 studies screened, 22 met the inclusion criteria. Our analysis indicates that gliomas are frequently underdiagnosed (i.e., the correct diagnosis is missed) and, though less extensively reported, also misdiagnosed (i.e., assigned incorrect diagnoses). Overall, diagnostic errors ranged from 1.9 to 11.6% with a median of 5% (mis-/underdiagnosis: 5%, range 1.9-20%, misgrading: 11.6% range: 0.6-47%). While diagnosing gliomas is complex, evidence suggests that clinical tools such as MRI and pathological methods, including neuroendoscopic biopsy and specific cytology techniques, can achieve high diagnostic accuracy. Diagnostic errors were linked to relevant patient consequences, with both overtreatment and undertreatment commonly reported. Conclusion Diagnostic errors in gliomas are common and can have serious implications for patients. More rigorous data are needed to better understand the causes of these errors, which is key for reducing misdiagnosis and underdiagnosis in the future.
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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 | 0.031 | 0.170 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".