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Record W4205384482 · doi:10.1080/0284186x.2022.2025613

Comparing treatment modalities for hepatocellular carcinoma: the value of network meta-analyses

2022· article· en· W4205384482 on OpenAlexaff
Ronald Chow, Charles B. Simone, Meghan P. Jairam, Anand Swaminath, Gabriel Boldt, Michael Lock

Bibliographic record

VenueActa Oncologica · 2022
Typearticle
Languageen
FieldMathematics
TopicStatistical Methods in Clinical Trials
Canadian institutionsWestern UniversityJuravinski Cancer CentreLondon Health Sciences CentreMcMaster UniversityUniversity of Toronto
Fundersnot available
KeywordsMedicineHepatocellular carcinomaValue (mathematics)Treatment modalityOncologyCarcinomaModalitiesInternal medicineStatistics

Abstract

fetched live from OpenAlex

We thank Rizzo and Brandi for their review and comments [1] on our article [2]. We would like to add that, in addition to the other treatment modalities listed by Rizzo and Brandi, external beam ra...

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)0.110
metaresearch head score (Gemma)0.264
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.110
Threshold uncertainty score0.582

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1100.264
Meta-epidemiology (narrow)0.0050.002
Meta-epidemiology (broad)0.0170.030
Bibliometrics0.0040.005
Science and technology studies0.0010.002
Scholarly communication0.0060.007
Open science0.0040.004
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0040.000

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 head0.907
GPT teacher head0.596
Teacher spread0.311 · 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 designMeta-analysis
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

Citations2
Published2022
Admission routes1
Has abstractno

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