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Additional file 1 of Rapid monitoring of health services use following a policy to switch patients from originator to biosimilar etanercept—a cohort study in British Columbia

2022· article· en· W6977393001 on OpenAlexaffabout

Bibliographic record

VenueFigshare · 2022
Typearticle
Languageen
FieldImmunology and Microbiology
TopicBiosimilars and Bioanalytical Methods
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsBiosimilarCohortCohort studyCumulative incidenceTable (database)Cumulative doseOff-label use

Abstract

fetched live from OpenAlex

Additional file 1. Supplementary Table 1. Cohorts construction: exclusion criteria. Supplementary Table 2. Drugs by therapeutic group. Supplementary Figure 1. Cumulative quantity of etanercept (originator or biosimilar) dispensed (mg per patient) over the follow-up period, by cohort. Supplementary Figure 2. Cumulative number of days on conventional synthetic disease modifying anti-rheumatic drugs (csDMARDs) per patient, over the follow-up period, by cohort. Supplementary Figure 3. Cumulative number of days on oral steroids per patient, over the follow-up period, by cohort. Supplementary Figure 4. Cumulative number of days on nonsteroidal anti-inflammatory drugs (NSAIDs) per patient, over the follow-up period, by cohort.

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)0.002
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.720
Threshold uncertainty score0.557

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.026
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.005
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.6620.040

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 head0.025
GPT teacher head0.289
Teacher spread0.264 · 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 designNot applicable
Domainnot available
GenreDataset

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
Published2022
Admission routes2
Has abstractyes

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