Learning From Mistakes: Importance of a Multidisciplinary Group, A Case Report
Bibliographic record
Abstract
There have been significant advances in the diagnosis and treatment of breast cancer over the past 20 years, due to increased knowledge about the biology and molecular changes in breast cancer. These advances have increased the complexity of treatment decision-making for individual women, and reinforced the need for a team approach to treatment decision-making. We report the case of an 80-year-old woman with a recidive invasive ductal breast carcinoma of high grade. In October 2015, she discovered an indolent breast bulk through self-examination and in the December of the same year, after the routine staging exams, she undergone a quadrantectomy and a limphoadenectomy. In March 2016, the patient was sent to our structure for a cycle of radiation therapy by her oncologist, even though a suspected lesion was seen on the thoracic wall on recent computed tomography scans. Our aim was to show an example about the importance of collaboration and multidisciplinary group in treating cancer.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".