Bibliographic record
Abstract
As a young, hospital-based physical therapist in the early 1970s, I vividly recall a middle-aged patient who had undergone a radical mastectomy for treatment of breast cancer. Her chest was sunken and hollow following removal of the pectoral muscles, overlying skin, and supraclavicular lymph nodes. She had a gaping wound in her axilla where the lymph nodes had been cleared, making the range-of-motion exercises that I provided extremely painful. For almost 80 years, the Halsted radical mastectomy was the standard treatment for breast cancer in North America, a procedure that was both physically and psychologically debilitating. An American surgeon named Bernard Fisher chose to challenge the myth that radical mastectomy was the only viable treatment for breast cancer. Through a series of large randomized controlled trials conducted in the 1970s and 1980s, Fisher's research team demonstrated that a much less invasive surgery—the partial mastectomy (lumpectomy), combined with radiation to the breast—was as effective as the radical mastectomy in prolonging lives of women with early-stage breast cancer.1,2 As a result, almost 75% of the more than 200,000 women diagnosed with breast cancer in North America each year can opt for a lumpectomy.3 For those who still require removal of the breast, much less invasive procedures—such as the total mastectomy or modified radical mastectomy—are now standard practice.
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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 | 0.025 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.072 |
| Scholarly communication | 0.014 | 0.020 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.018 | 0.055 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".