Avoiding Cognitive Bias in Radiology: New Brain Lesions in Homeopathically Treated Breast Cancer Patient
Bibliographic record
Abstract
Cognitive biases can impact diagnostic accuracy and timeliness of medical care.Crucially, these biases can be mitigated by carefully integrating clinical data.Herein, we present a case of a patient with metastatic breast cancer treated with homeopathic therapy, who presented with new hyperdense non-enhancing brain lesions on CT.This case highlights the risks of framing bias for radiologists when little clinical information is available and underscores the necessity of a thorough evaluation and review of clinical history to identify alternative diagnoses and ensure timely and accurate management.It also highlights the expected imaging appearance of treated versus untreated intracranial breast cancer metastases.An adult female patient with ERþ/HER2-invasive breast ductal carcinoma with chest wall and spine metastases presented with acute delirium several weeks after posterior spine decompression surgery for cord compression due to epidural metastasis.Thus far, she had been treated with homeopathic remedies without a conventional chemotherapy regimen.Her postoperative course was complicated by mildly decreased sodium level likely in the context of the syndrome of inappropriate antidiuretic hormone secretion and dehiscence of the surgical wound, for which she was followed by a wound care nurse.Approximately two weeks after the surgery, she became acutely confused and disoriented in a setting of still mildly decreased sodium level, a positive urinalysis and mild fever.Unenhanced CT brain performed in the emergency department showed multiple intra-axial hyperattenuating foci (Figure 1a), initially appropriately considered hemorrhagic metastases without differential considerations.Radiation oncology was consulted to manage further care.A subsequent gadoliniumenhanced MRI head showed multiple intraparenchymal foci of susceptibility without enhancement (Figure 1b,c) that were first considered metastases but then also prompted consideration of septic emboli as an alternative diagnosis since untreated breast cancer metastases should enhance.The radiology report triggered an infectious disease consult and a workup for a potential infectious source.Urine and blood cultures grew Staphylococcus aureus.MRI of the spine showed expected postoperative findings without evidence of abscess.A transesophageal echocardiogram then revealed the presence of mitral valve vegetation that was later
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".