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Record W4387874130 · doi:10.3390/diagnostics13203283

Spectrum of Imaging Patterns of Lung Cancer following Radiation Therapy

2023· review· en· W4387874130 on OpenAlexaff
Taylor A. Strange, Lauren T. Erasmus, Jitesh Ahuja, Rishi Agrawal, Girish S. Shroff, Mylene T. Truong, Chad D. Strange

Bibliographic record

VenueDiagnostics · 2023
Typereview
Languageen
FieldMedicine
TopicLung Cancer Diagnosis and Treatment
Canadian institutionsMcGill University
Fundersnot available
KeywordsRadiation therapyMedicineProton therapyLung cancerRadiologyRadiological weaponChemoradiotherapyLungMedical physicsOncologyInternal medicine

Abstract

fetched live from OpenAlex

Radiation therapy using conventional or newer high-precision dose techniques, including three-dimensional conformal radiotherapy, intensity-modulated radiation therapy, stereotactic body radiation therapy, four-dimensional conformational radiotherapy, and proton therapy, is an important component of treating patients with lung cancer. Knowledge of the radiation technique used and the expected temporal evolution of radiation-induced lung injury, as well as patient-specific parameters such as previous radiotherapy, concurrent chemoradiotherapy, or immunotherapy, is important in image interpretation. This review discusses factors that affect the development and severity of radiation-induced lung injury and its radiological manifestations, as well as the differences between conventional and high-precision dose radiotherapy techniques.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.038
GPT teacher head0.391
Teacher spread0.353 · 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

Citations11
Published2023
Admission routes1
Has abstractyes

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