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Record W4214922303 · doi:10.1016/j.jncc.2022.02.003

Radiotherapy for small cell lung cancer in current clinical practice guidelines

2022· review· en· W4214922303 on OpenAlexaboutno aff
Haiyan Zeng, Dirk K.M. De Ruysscher, Xiao Hu, Danyang Zheng, Yang Li, Umberto Ricardi, Feng‐Ming Kong, Lizza Hendriks

Bibliographic record

VenueJournal of the National Cancer Center · 2022
Typereview
Languageen
FieldMedicine
TopicLung Cancer Research Studies
Canadian institutionsnot available
Fundersnot available
KeywordsGuidelineMedicineRadiation therapyProphylactic cranial irradiationStage (stratigraphy)Conventional PCILung cancerMedical physicsRadiologyOncologyInternal medicinePathology

Abstract

fetched live from OpenAlex

Several guidelines including radiotherapy recommendations exist worldwide for the treatment of small cell lung cancer (SCLC). To evaluate the differences in radiotherapy recommendations we conducted a systematic review. PubMed and the sites of medical societies were searched for SCLC guidelines published in either English, Chinese, or Dutch. This was limited to January 2018 till February 2021 to only include up-to-date recommendations. Data was extracted and compared regarding the guideline's development method and radiotherapy recommendations. Eleven guidelines were identified (PubMed n=4, societies n=7) from Spain (n=1), Canada (n=1), America (n=3), United Kingdom (n=1), the Netherlands (n=1), and China (n=3), respectively. Nine guidelines assessed the strength of evidence (SOE) and specified the strength of recommendation (SOR), although methods were different. The major radiotherapy recommendations are similar although differences exist in thoracic radiotherapy (TRT) dose, time, and volume. Controversial areas are TRT in resected stage I-IIA (pN1), prophylactic cranial irradiation (PCI) in resected as well as unresected stage I-IIA, stereotactic body radiation therapy (SBRT) in unresected stage I-IIA, PCI time, and PCI versus magnetic resonance imaging (MRI) surveillance in stage IV. The existence of several overlapping guidelines for SCLC treatment indicates that guideline development is (unnecessarily) repeated by different organizations or societies. Improvement could be made by better international collaboration to avoid duplicating unnecessary work, which would spare a lot of time and resources. Efforts should be made to work together on controversial or unknown fields.

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)0.003
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation 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 score0.669
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.372
GPT teacher head0.626
Teacher spread0.254 · 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 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

Citations5
Published2022
Admission routes1
Has abstractyes

Explore more

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