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Record W4406081463 · doi:10.1007/s12325-024-03087-2

Correction to: Indirect Treatment Comparisons in Healthcare Decision Making: A Targeted Review of Regulatory Approval, Reimbursement, and Pricing Recommendations Globally for Oncology Drugs in 2021–2023

2025· review· en· W4406081463 on OpenAlexaff
Ataru Igarashi, Shiro Tanaka, Raf De Moor, Nan Li, Mariko Hirozane, David Bin-Chia Wu, Li Wen Hong, Dae Young Yu, Mahmoud Hashim, Brian Hutton, Krista Tantakoun, Christopher Olsen, Fatemeh Mirzayeh Fashami, Imtiaz A. Samjoo, Chris Cameron

Bibliographic record

VenueAdvances in Therapy · 2025
Typereview
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsEVERSANA (Canada)Ottawa Hospital
Fundersnot available
KeywordsMedicineReimbursementHealth careIntensive care medicineOncologyInternal medicineDrug approvalFamily medicinePharmacologyDrug

Abstract

fetched live from OpenAlex

Since the time of

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)0.021
metaresearch head score (Gemma)0.256
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.979
Threshold uncertainty score0.750

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.256
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0050.005
Bibliometrics0.0080.010
Science and technology studies0.0010.002
Scholarly communication0.0050.004
Open science0.0040.003
Research integrity0.0050.008
Insufficient payload (model declined to judge)0.2240.055

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 head0.277
GPT teacher head0.538
Teacher spread0.261 · 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 designSystematic review
DomainMethods
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

Citations0
Published2025
Admission routes1
Has abstractno

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