Diagnostic implications of histological analysis of neurosurgical aspirate in addition to routine resections
Bibliographic record
Abstract
Many neurosurgical centers use surgical aspirators to remove brain tumor tissue. The resulting aspirate consists of fragmented viable tumor, normal or tumor-infiltrated brain tissue as well as necrotic tissue, depending on the type of tumor. Typically, such fragmented aspirate material is collected but discarded and not included when making the histopathological diagnosis. Whereas the general suitability of surgical aspirate for histological diagnosis and immunohistochemical staining has been reported previously, we have systematically investigated whether the collection and histological examination of surgical aspirate has an impact on diagnosis, in particular on the tumor grading, by providing additional features. Surgical and aspirate specimens from 85 consecutive neurosurgical procedures were collected and routinely processed. Sixty-five of the 85 specimens were intrinsic brain tumors and the remainder consisted of metastatic tumors, meningiomas, schwannomas and lymphomas. Important diagnostic features seen in surgical aspirate were microvascular proliferation (n = 3), more representative necrosis (n = 2), and gemistocytic component (n = 2). In one case, microvasular proliferations were seen in the aspirate only, leading to a change of diagnosis. Collection of surgical aspirate also generates additional archival material which can be microdissected and used for tissue microarrays or for molecular studies.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 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 | 0.001 |
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".