Bibliographic record
Abstract
A 1990 National Institutes of Health consensus conference concluded that appropriate conservative therapy for breast cancer includes postexcision breast radiotherapy. However, patterns-of-care studies show that, although more patients are being treated with breast-conserving surgery, the use of radiotherapy in this setting is declining and the likelihood of receiving radiotherapy is related to insurance, race, income, and distance from radiotherapy centers (1–4). To help alleviate the strain on patient and institutional resources that a 5- to 6-week radiotherapy treatment course creates, several Canadian institutions have explored delivery of shorter radiotherapy regimens, demonstrating acceptable cosmesis and local control of breast cancer in prospective nonrandomized and retrospective matched-control series. Although radiobiologic principles dictate that, given enough dose per fraction and total dose, radiation therapy delivered in more rapid treatment schedules can be as effective in controlling tumor recurrence as longer schedules, the issue is whether the toxicity and cosmesis will remain acceptable. In this issue of the Journal, Whelan et al. (5) report the initial results of a randomized comparison of two radiation fractionation schedules—42.5 Gy in 16 fractions over 22 days (2.65 Gy/day) and a standard regimen of 50 Gy in 2-Gy fractions over 35 days—in lymph node-negative, margin-negative breast cancer patients. The shortened course did not result in excess ipsilateral breast tumor recurrence or in worse cosmesis. At a median follow-up of 69 months, the 5 year in-breast recurrence (invasive and in situ) rates were 4% in both treatment arms. This recurrence rate is similar to the recurrence rate seen in a nonrandomized prospective trial in Vancouver (6) of 186 lymph node-negative patients treated with 44 Gy in 16 fractions (2.75 Gy per fraction), which was 6% at a median follow-up of 6.7 years, and to the recurrence rate seen in a retrospective analysis from Ontario of 294 patients treated with 40 Gy in 16 fractions (2.5 Gy per fraction), which was 3.5% at a median follow-up of 5.5 years (7).
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.
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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.012 | 0.018 |
| 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".