Thermal Therapy, Part 1: An Introduction to Thermal Therapy
Bibliographic record
Abstract
Thermal therapy is widely known and electromagnetic (EM) energy, ultrasonic waves, and other thermal-conduction-based devices have been used as heating sources. In particular, advances in EM technology have paved the way for promising trends in thermotherapeutical applications such as oncology, physiotherapy, urology, cardiology, ophthalmology, and in other areas of medicine as well. This series of articles is generally written for oncologists, cancer researchers, medical students, biomedical researchers, clinicians, and others who have an interest in this topic. This article reviews key processes and developments in thermal therapy with emphasis on two techniques, namely, hyperthermia [including long-term low-temperature hyperthermia (40-41 degrees C for 6-72 hr), moderate-temperature hyperthermia (42-45 degrees C for 15-60 min), and thermal ablation, or high-temperature hyperthermia (> 50 degrees C for > 4-6 min)]. The article will also provide an overview of a wide range of possible mechanisms and biological effects of heat. This information will be discussed in light of what is known about the degree of temperature rise that is expected from various sources of energy. The review concludes with an evaluation of human exposure risk to EM energy or the corresponding heat, trends in equipment development, and future research directions.
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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.014 |
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".