Cognitive biases in orbital mass lesions – Lessons learned
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.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".