Bibliographic record
Abstract
In Reply Thank you for your interest in our cerebellopontine angle lipoma case report. You are correct that either CT or MR can be used to diagnose lipomas. Both are not required. Lipomas have a negative Hounsfield unit measure on CT. On MR, lipomas are T1 hyperintense, T2 variable intensity but usually hyperintense, do not enhance following contrast, and demonstrate signal loss on both T1 and T2 with fat saturation. You are correct that using fat saturation on the postcontrast T1 sequences is more convenient for assessing enhancement. However, careful expert comparison of corresponding slices in multiple planes of the precontrast T1 sequences without fat saturation to the postcontrast T1 sequences without fat saturation can also exclude enhancement. Matthew G. Crowson, M.D. Department of Otolaryngology–Head and Neck Surgery University of Toronto Sunnybrook Health Sciences Centre Toronto, Ontario, Canada Sean P. Symons, M.P.H., M.D., F.R.C.P.C., D.A.B.R. Department of Otolaryngology–Head and Neck Surgery University of Toronto Sunnybrook Health Sciences Centre Toronto, Ontario, Canada Division of Neuroradiology Department of Medical Imaging University of Toronto Sunnybrook Health Sciences Centre Toronto, Ontario, Canada Joseph M. Chen, M.D., F.R.C.S.C. Department of Otolaryngology–Head and Neck Surgery University of Toronto Sunnybrook Health Sciences Centre Toronto Ontario, Canada [email protected]
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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.013 |
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; both teacher heads agree on what is shown here.
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".