Bibliographic record
Abstract
While brachytherapy is the oldest form of radiation therapy, it is also a very exciting field from both physics and clinical perspectives. From the physics standpoint, brachytherapy dosimetry is largely being governed by the inverse-square law, leading to an unparalleled dose deposition kernel (dose emitted by a seed or single dwell position), even compared to proton or heavy-ion beamlets. There is slightly more dose beyond the central deposition point, but comparatively very little prior to it, that is, little or no entrance dose! It is easy to sum multiple dwell positions that cover a tumor, and the intensity can be modulated quite effectively using dwell times. From a clinical perspective, what sets brachytherapy apart from other intraoperative modalities (e.g., laser, radiofrequency, cryogenic) is our ability to precisely calculate the energy deposited across the relevant patient geometry, anticipate the effect from known dose-outcome relationships, and deliver that energy with exquisite control and selectively to the target volume while sparing organs at risks. This targeting ability has improved substantially over the last two decades. It is built upon key foundational elements, many of which stem from the research and development within our medical physics community. This article provides an overview of these elements that combine to make brachytherapy a successful and developing radiotherapy modality.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".