Confusion Between Bitemporal Hemianopia and Cecocentral Scotoma
Bibliographic record
Abstract
We appreciate Dr. Horton's comments. In addition to the numerous cases of ethambutol-induced bitemporal hemianopia reported in the past, animal studies have also revealed chiasmal damage affecting both the crossing and the noncrossing axons in experimentally induced ethambutol toxicity (1). Kho et al (2) characterized ethambutol-induced bitemporal hemianopia in a series of patients, and this study has shown the existence of bitemporal visual field defects with or without superimposed central/cecocentral scotomas, and in those without coexisting central or cecocentral scotomas, the visual field defects were highly suggestive of chiasmal injury. Although a cecocentral scotoma can occasionally be confused with bitemporal hemianopia, the bitemporal visual field defect in our case plausibly aligned along the vertical midline, and it is not confined to just the central portion of the temporal fields. We agree that the poor visual acuity in our patient cannot be explained by the chiasmal lesion alone, and there may also be additional involvement of the adjacent parts of the optic nerves as described previously in a histopathological study (1). But, this was not evident radiographically. Magnetic resonance images (MRI) from our article were reviewed again by our neuroradiologist and also in a “masked” fashion by 3 other neuroradiologists who had not seen the imaging and were not aware of the case. These neuroradiologists readily and unanimously concluded that 1) the original MRI was clearly abnormal with increased T2 signal within the chiasm and was also mildly swollen, and 2) the signal intensity within the chiasm normalized on the follow-up study. This imaging finding correlates with our patient's bitemporal hemianopia.
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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 | 0.003 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.015 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".