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Record W4320716061 · doi:10.1016/j.eclinm.2023.101852

Letter to Editor-response: placebo response rates

2023· letter· en· W4320716061 on OpenAlexafffund
Bishal Gyawali

Bibliographic record

VenueEClinicalMedicine · 2023
Typeletter
Languageen
FieldMedicine
TopicSarcoma Diagnosis and Treatment
Canadian institutionsQueen's University
FundersGovernment of OntarioOntario Institute for Cancer Research
KeywordsMedicinePlaceboPlacebo responseClinical trialInternal medicineRandomized controlled trialOncologyPathologyAlternative medicine

Abstract

fetched live from OpenAlex

We'd like to thank van der Graaf et al. for their thoughtful comments and feedback on our article analyzing the response rate from placebo in cancer drug clinical trials.1 The overall response rate from placebo was 1% across the trials that included multiple cancer histology. Subgroup analysis revealed a higher response rate of 4% in sarcoma trials. Van der Graaf et al. are correct that this was affected predominantly by the high placebo response seen in the trial by Gounder et al. in desmoid tumors, an explanation that has been included in the updated version of the article.

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)0.008
metaresearch head score (Gemma)0.077
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: Not applicable
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.032
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.077
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0030.003
Open science0.0020.001
Research integrity0.0320.025
Insufficient payload (model declined to judge)0.0120.014

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 head0.058
GPT teacher head0.385
Teacher spread0.327 · 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
GenreEditorial

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

Citations0
Published2023
Admission routes2
Has abstractyes

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