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Record W2916511951 · doi:10.12968/npre.2017.15.1.10

Research roundup: January 2017

2017· article· en· W2916511951 on OpenAlexaboutno aff
Mark Greener

Bibliographic record

VenueNurse Prescribing · 2017
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical Practices and Patient Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineDementiaCognitive impairmentQuarter (Canadian coin)Nursing homesGerontologyType 2 diabetesDiabetes mellitusMedication errorPsychiatryCognitionInternal medicineHealth careNursingDiseasePatient safetyEndocrinology

Abstract

fetched live from OpenAlex

In brief About a quarter of nursing home residents experience a medication error, while three-quarters receive a potentially inappropriate drug, a new systematic review shows About two-thirds of falls among older people occur in those who took at least one high-risk medication during the preceding 24 hours Individuals with albuminuria are at increased risk of cognitive impairment and dementia Duck-billed platypus venom could lead to a new drug for type 2 diabetes

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)0.019
metaresearch head score (Gemma)0.090
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: Review · Consensus signal: none
Teacher disagreement score0.656
Threshold uncertainty score0.491

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.090
Meta-epidemiology (narrow)0.0030.003
Meta-epidemiology (broad)0.0050.005
Bibliometrics0.0080.007
Science and technology studies0.0030.001
Scholarly communication0.0110.007
Open science0.0050.007
Research integrity0.0150.009
Insufficient payload (model declined to judge)0.6560.466

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 head0.505
GPT teacher head0.543
Teacher spread0.038 · 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
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

Citations1
Published2017
Admission routes1
Has abstractyes

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