The clinical implementation of a method for calculating the output factor and per cent depth dose for an electron beam
Bibliographic record
Abstract
A modified sector-integration method has been developed that predicts electron beam output factor at any point on the beam central axis, for a given source to surface distance (SSD), as a function of the geometry of the irradiated field. The main concept of this method is that with the arbitrary field shape divided into small sectors, the individual contributions from each sector can be calculated based on the sector radius, using a dataset consisting of circular inserts of standard radii. A computer program was developed based on this algorithm. The program interfaces to a digital camera that is used to capture the shape of the electron insert. We compared the calculated and the measured output factors and per cent depth doses (PDDs) at different SSDs for various rectangular inserts and a typical irregularly shaped insert used in our clinic. To determine the geometric limitations of this algorithm, a series of rectangular inserts were designed with the long-to-short axis ratio between 1:1 and 7:1. The agreement between calculation and measurement for the electron output and PDD was generally within 2% (or 2 mm) for energies from 6 to 20 MeV.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".