Author Response: Pearls & Oy-sters: Isolated Oculomotor Nerve Palsy due to Pituitary Apoplexy Missed on CT Scan
Bibliographic record
Abstract
We appreciate the comments and helpful tips by Dr. Zhang et al. on our paper.1 We agree with their comment that a negative CT scan cannot rule out the possibility of a subarachnoid hemorrhage (SAH) or apoplexy, which makes having a low threshold for follow-up scanning with MRI imperative. Had the MRI scan also been unremarkable, we agree that it would have been reasonable to consider a lumbar puncture to explore a possible bleed in the context of a severe headache and focal neurologic signs before attributing it to other, less-serious etiologies. Interestingly, some subarachnoid hemorrhages may be due to non-aneurysmal events of venous origin, particularly in the perimesencephalic areas and not necessarily due to missed aneurysms.2 Finally, it is important to note that a critical take away from this case is that pituitary apoplexy can mimic a subarachnoid hemorrhage presenting with a third nerve palsy, and it is, therefore, important to have on the differential diagnosis of patients presenting with thunderclap headache, in addition to the typically considered diagnosis of SAH.
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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 | 0.002 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.032 | 0.022 |
| Insufficient payload (model declined to judge) | 0.010 | 0.007 |
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".