An Internet of Things app for monitor unit calculation in superficial and orthovoltage skin therapy
Bibliographic record
Abstract
Abstract Purpose: We developed an app for Internet of Things (IoT) device such as smartphone or tablet to calculate the monitor unit in superficial and orthovoltage skin therapy. The app can run both on the Windows and Android operation system. Methods: The IoT app was created based on the formula to calculate the monitor unit in skin therapy using kV photon beams. The calculation was based on databases of dose variables such as relative exposure factor and backscatter factor. The calculation also considered the stand-off and stand-in correction according to the inverse-square and inverse-cube law. Verification of the app was carried out by comparing the monitor unit results with those from hand calculations. Results: The frontend window of the app provided a user-friendly interface to the user for inputting prescription dose, beam and treatment setup variables. The user could save the calculation record electronically, generate a printout or send it to other radiation staff using the IoT. Verification of the app showing that deviation between the monitor units calculated by the app and by hand is insignificant. Conclusion: The verified IoT app can effectively calculate the monitor unit in superficial and orthovoltage skin therapy. The app takes advantages of all innate features of IoT such as real time communication, Internet access, data transfer and sharing.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.025 | 0.007 |
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".