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Record W4393040969 · doi:10.1002/pro6.1222

Stereotactic radiotherapy: An educational narrative review

2024· review· en· W4393040969 on OpenAlexaff
Agha Muhammad Hammad Khan, Benjamin Li, Fatima Shaukat, Yumna Ahmed, Mahmoud Mohamed Alfishawy, Iqbal Alamri, Rashid Bashir, Furhan Altaf, Sercan Yılmaz, Jamsari Khalid, Zahid Al Mandhari, Nadeem Pervez, Salim Chaib Rassou, Layth Mula‐Hussain

Bibliographic record

VenuePrecision Radiation Oncology · 2024
Typereview
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsDalhousie UniversityMcGill University
Fundersnot available
KeywordsNarrativeNarrative reviewStereotactic radiotherapyMedical physicsRadiation therapyMedicinePsychologyRadiosurgeryRadiologyPsychotherapistArtLiterature

Abstract

fetched live from OpenAlex

Stereotactic radiotherapy is a term collectively used to describe the radiation treatment techniques that allow for the delivery of highly precise ionizing radiation. It is usually a high dose per session in single or few fractions. This treatment approach has been in medical use for over six decades and has primarily evolved in the last two decades. Many patients benefit from this unique non-conventional radiotherapy approach. Its indications include various malignant, benign and functional problems in cranial and body sites. This technique is not widespread in developing countries compared to developed countries. This work is an educational narrative review for learners in radiation oncology. We aim to share the knowledge of this practice to improve precision radiation oncology globally. This review summarizes the basics of stereotactic radiotherapy, the technical prerequisites, the clinical considerations, the practical recommendations and the learning points from each site-specific region.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.009
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.002

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.

Opus teacher head0.036
GPT teacher head0.460
Teacher spread0.424 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations6
Published2024
Admission routes1
Has abstractyes

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