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Record W2897125715 · doi:10.1093/annonc/mdy472

Avoiding the hazards of misinterpreting treatment effects

2018· letter· en· W2897125715 on OpenAlexaff
Everardo D. Saad, Ian F. Tannock

Bibliographic record

VenueAnnals of Oncology · 2018
Typeletter
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Financial Impacts of Cancer
Canadian institutionsPrincess Margaret Cancer CentreUniversity of Toronto
FundersEuropean Society for Medical OncologyAmerican Statistical Association
KeywordsMedicineIntensive care medicine

Abstract

fetched live from OpenAlex

Time-to-event end points are the most frequent outcome measures in oncology phase III trials. Since these trials inform clinical practice, a clear understanding of the measures used to describe the effect of treatment on such outcomes is important for oncologists, who need to communicate appropriately with patients and their families [1]. The effect of treatment can be expressed in absolute and in relative terms, depending on individual preference, the frequency of events, and accepted standards in a given field.

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)0.455
metaresearch head score (Gemma)0.789
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.545
Threshold uncertainty score0.672

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.4550.789
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0050.004
Bibliometrics0.0130.006
Science and technology studies0.0020.009
Scholarly communication0.0090.011
Open science0.0050.008
Research integrity0.0060.012
Insufficient payload (model declined to judge)0.0080.004

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 head0.086
GPT teacher head0.330
Teacher spread0.244 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designTheoretical or conceptual
DomainMethods
GenreCommentary

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
Published2018
Admission routes1
Has abstractyes

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