Diagnostic implications of histological analysis of neurosurgical aspirate in addition to routine resections
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 it