Cognitive biases in orbital mass lesions – Lessons learned
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE: A patient's presentation and clinical diagnosis can at times be clouded by their past medical history. Clinicians' anchoring bias towards initial information, such as a history of cancer, may lead them astray when creating a differential diagnosis for a patient who presents with new signs and symptoms of a mass lesion, assuming metastatic disease without seeking tissue confirmation. METHODS: The presentation, workup, diagnosis, and treatment of two patients who presented with orbital masses in the context of a primary prostate cancer are presented in this report. RESULTS: In both cases, prostate cancer metastasis to the orbit was top on the differential. Ultimately, histopathological examination of biopsies taken from the orbital masses revealed orbital lymphoma in both patients. CONCLUSION: With mounting rates of patients who have survived a previous cancer, multiple primary cancers within one patient are becoming increasingly common. While prostate cancer metastasis to the orbit is a relatively rare event, orbital lymphoma is a more common diagnosis in orbital masses. Therefore, when patients present with orbital masses in the context of prostate cancer, the conclusion should not immediately be metastasis and a tissue diagnosis should be sought; especially given that the treatment of these entities is different.
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.
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 | 0.000 | 0.000 |
| 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) | 0.003 | 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