Usefulness of preoperative extracranial imaging in radiologically suspicious glioblastoma in the West of Scotland and proposal of an imaging diagnostic pathway
Bibliographic record
Abstract
Introduction A computed tomography chest, abdomen and pelvis scan (CT CAP) is probably unnecessary if a glioblastoma is detected on the initial CT brain (CTB) before more radiologically definitive magnetic resonance imaging (MRI). We audited its frequency to develop and improve our diagnostic management pathway. Methods Twelve-month retrospective case series from 2018 of patients having an initial CTB suspicious for glioblastoma. We dichotomised patients into two groups: Group 1, tissue proven; and Group 2, non-tissue proven, owing to increased extracranial comorbidity in Group 2, which might influence a medical decision to request a CT CAP despite the radiological diagnosis of a glioblastoma being obvious on an initial CT. We quantified the frequencies of plain and contrast CTBs, CT CAPs and extracranial malignancy. Results In total, 131 patients had a CTB suspicious for glioblastoma; of these, 72% had a CT CAP and 17% had extracranial malignancy. In Group 1 (n = 84 [mean age 59 years]), 64% had a CT CAP. Plain CTB was undertaken in 24% of patients and contrast CTB in 76%. Extracranial malignancy was present in 8% and 12%. In Group 2 (n = 47 [mean age 73 years]), 85% had a CT CAP. Plain CTB was undertaken in 22% of patients and contrast CTB in 78%. Extracranial malignancy was present in 33% and 23%. Negative CT CAPs were found in ∼88% of CTBs in Group 1 and ∼75% of CTBs in Group 2. Conclusions Patients having an initial contrast CTB suggestive of glioblastoma, prior to definitive MRI, who are going to be managed surgically, having no history of extracranial malignancy, do not necessarily need a CT CAP unless MRI is non-diagnostic.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".