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Record W4398216091 · doi:10.29409/ijcmg.v16i2.343

Ethics-Guided Radiation Therapy (EGRT): A necessity in radiation oncology practice

2023· article· en· W4398216091 on OpenAlexaff
Layth Mula‐Hussain

Bibliographic record

VenueIraqi Journal of Cancer and Medical Genetics · 2023
Typearticle
Languageen
FieldMedicine
TopicAdvances in Oncology and Radiotherapy
Canadian institutionsDalhousie UniversityCape Breton University
Fundersnot available
KeywordsRadiation oncologyRadiation therapyMedical physicsMedicineOncologyEngineering ethicsInternal medicineEngineering

Abstract

fetched live from OpenAlex

Medical ethics principles have been the basis of medical practice since early human civilization. The well-accepted principles are autonomy, beneficence, non-maleficence, and justice. With the advancement of academia, industry, medicine, and technology, there is a need to empower ethics-guided radiation therapy (EGRT). A PubMed search was done on Oct. 22, 2023, using the words: (“Radiotherapy”[Mesh]) AND “Ethics, Clinical”[Mesh]) and the results were a total of 58. Among these, 17 titles seem to be in relation, but only a handful were of intimate relation to ethics and radiotherapy. An additional handful of non-PubMed references were found. EGRT, in my opinion, is a new acronym for an old concept that needs further elaboration and experts’ consensus in the modern radiation oncology literature. In parallel with the technological advances in radiotherapy, like intensity-modulated radiation therapy “IMRT” and image-guided radiation therapy “IGRT,” we are aiming to create an initiative to establish EGRT to be like a model that every radiation oncologist can follow in the daily radiotherapy practice. The coming work will be composed of an extensive literature review, international survey, and expert consensus, and it is intended to be a base for further efforts in this aspect.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.959
Threshold uncertainty score0.439

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.050
GPT teacher head0.502
Teacher spread0.452 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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