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Record W6976880958 · doi:10.6084/m9.figshare.13175906

Knowledge and awareness of biosimilars and shared decision-making among gastroenterology team members in Colorado, USA

2020· article· en· W6976880958 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2020
Typearticle
Languageen
FieldImmunology and Microbiology
TopicBiosimilars and Bioanalytical Methods
Canadian institutionsnot available
Fundersnot available
KeywordsBiosimilarNocebo EffectReading (process)NoceboQuarter (Canadian coin)

Abstract

fetched live from OpenAlex

<b>Background</b>: There are gaps in gastroenterologist team members’ understanding of various topics related to biosimilars. We aimed to examine perspectives, views, and attitudes toward biosimilar and shared decision-making (SDM) among gastroenterology team members in Colorado, USA. The ultimate goal was to increase knowledge and awareness of biosimilars and SDM. <b>Research design and methods</b>: We developed educational materials focused on biosimilars and SDM and distributed them to each participating gastroenterology office. Subsequently, we conducted a survey of all team members from participating offices. <b>Results</b>: Responses were obtained from 54 gastroenterology team members. Most respondents identified the correct answer regarding biosimilars, the nocebo effect, and SDM. Almost half (47.2%) of respondents scored their level of awareness regarding biosimilars prior to reading our educational materials as poor, and nearly one quarter (26.4%) indicated so for SDM. Improvement in scores after reading our materials was significant for both biosimilars and SDM (i.e. biosimilar: z = 6.276, p-value &lt;0.001 and SDM z = 6.328, p-value &lt;0.001). <b>Conclusions</b>: Educational efforts effectively increased the low baseline knowledge and awareness of biosimilars and SDM among gastroenterology team members. More educational projects focused on biosimilars and SDM are needed to reduce the nocebo effect and prevent hampering of the cost-savings of biosimilars.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.300
Threshold uncertainty score0.979

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0220.000

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 head0.039
GPT teacher head0.313
Teacher spread0.274 · 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 teacher head, not a consensus.

Study designObservational
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

Citations0
Published2020
Admission routes1
Has abstractyes

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