Multi-Institutional Modified Delphi For Genomics in Expert Consensus Survey of Genomic Testing for Anterior Skull Base Malignancies
Bibliographic record
Abstract
Objectives: The use of genomic testing for patients with anterior skull base malignancies has grown dramatically. There are no clear guidelines on indications for testing. As the literature on the subject is still in early stages, there is a need for expert consensus. We conducted a modified Delphi expert consensus process with high-volume North American cranial base surgical programs. Design Setting Participants: A modified Delphi consensus approach was used, following the method laid out by the American Academy of Otolaryngology-Head and Neck Surgery, and included 13 high-volume care centers. An otolaryngologist was appointed at each location to serve as the institutional representative. Main Outcome Measures: Participant responses to Delphi surveys were tabulated to determine consensus. Results: Thirteen teams responded comprising 23 otolaryngologists and 10 neurosurgeons. Overall, 11 of 12 institutions reported genomic testing to be fairly or easily available at their location, and 22 of 38 initial statements achieved consensus. Statements achieving consensus focused on primary and recurrent rare tumors without possibility of margin-negative resection, those with family history of anterior skull base malignancies, or rare tumors with distant metastasis. Statements regarding routine genomic sequencing or for primary tumors and cost of care did not achieve consensus. Conclusion: Expert multidisciplinary teams agreed on several appropriate settings for genomic sequencing in patients with anterior skull base malignancies, including recurrence, distant metastasis, and the inability to achieve a margin-negative resection. Further research is needed to explicitly clarify the role of genomic sequencing in this rare disease group.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.266 | 0.198 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".