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Record W4281893891 · doi:10.1016/s1470-2045(22)00271-6

Stereotactic radiosurgery versus whole brain radiotherapy in patients with intracranial metastatic disease and small-cell lung cancer: a systematic review and meta-analysis

2022· review· en· W4281893891 on OpenAlexafffund
Karolina Gaebe, Alyssa Y. Li, Amy Park, Ambica Parmar, Benjamin H. Lok, Arjun Sahgal, Kelvin Chan, Anders W. Erickson, Sunit Das

Bibliographic record

VenueThe Lancet Oncology · 2022
Typereview
Languageen
FieldMedicine
TopicLung Cancer Research Studies
Canadian institutionsSt. Michael's HospitalPrincess Margaret Cancer CentreHealth Sciences CentreSunnybrook Health Science CentreOntario Institute for Cancer ResearchUniversity of Toronto
FundersCanadian Institutes of Health ResearchAmerican Association of Neurological SurgeonsUniversity of TorontoAstraZenecaCongress of Neurological SurgeonsPfizer
KeywordsMedicineRadiosurgeryMeta-analysisOncologyHazard ratioRadiation therapyInternal medicineLung cancerConfidence interval

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.007
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.985
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.018
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0150.039
Bibliometrics0.0030.005
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0050.000

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 head0.120
GPT teacher head0.422
Teacher spread0.303 · 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.

Study designMeta-analysis
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

Citations88
Published2022
Admission routes2
Has abstractno

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