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Record W3006383683 · doi:10.3389/fvets.2021.728267

Editorial: Antimicrobial Usage in Companion and Food Animals: Methods, Surveys and Relationships With Antimicrobial Resistance in Animals and Humans, Volume II

2021· editorial· en· W3006383683 on OpenAlexaff
Lucie Collineau, Carolee A. Carson, Miguel Á. Moreno

Bibliographic record

VenueFrontiers in Veterinary Science · 2021
Typeeditorial
Languageen
FieldEnvironmental Science
TopicPharmaceutical and Antibiotic Environmental Impacts
Canadian institutionsPublic Health Agency of Canada
Fundersnot available
KeywordsAntimicrobialAntibiotic resistanceBiologyVolume (thermodynamics)Veterinary medicineMicrobiologyMedicineAntibiotics

Abstract

fetched live from OpenAlex

Cognitive stimulation therapy (CST) is a manualized psychosocial group intervention for people with mild to moderate dementia. Because of its broad scientific evidence and cost effectiveness, CST is now used globally. To ensure replicability and quality standards of the intervention in other cultures, Aguirre et al. (2014) developed guidelines for cultural adaptation of CST based on the formative method for adapting psychotherapy (FMAP). Following this community-based approach, we adapted and translated the English CST manual into German, including multiprofessional focus groups, two adaptation cycles, and two pilot CST groups ( ; n = 13) in different settings representative of the German healthcare system. Effectiveness in both groups was assessed by pre-post comparison of standard scales on cognition, depression, quality of life, and self-efficacy. We were able to replicate previous findings of improved cognition as measured by the ADAS-Cog, with effect sizes in the same range as in previous randomized controlled trials. Additionally, self-efficacy increased in post-test compared to the pre-test, indicating that CST might trigger cognition through positive, self-rewarding activation.

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)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.317
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.003
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.030
GPT teacher head0.302
Teacher spread0.272 · 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

Citations5
Published2021
Admission routes1
Has abstractyes

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