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Record W2810456237 · doi:10.3399/bjgp18x697637

Long-term conditions and the National Diabetes Audit

2018· letter· en· W2810456237 on OpenAlexaff
Adrian Heald, Mike Stedman, Sanam Farman, Anthony A. Fryer, Sue Bailey, Roger Gadsby

Bibliographic record

VenueBritish Journal of General Practice · 2018
Typeletter
Languageen
FieldMedicine
TopicChronic Disease Management Strategies
Canadian institutionsHealth Sciences Centre
FundersWarwick Medical SchoolAcademy of Medical Royal CollegesUniversity of Warwick
KeywordsTerm (time)AuditMedicinePrimary careDiabetes mellitusMental healthMultimorbidityGerontologyFamily medicineData sciencePsychiatryChronic diseaseComputer science

Abstract

fetched live from OpenAlex

The recent article by Williams et al estimated that one clinical pharmacist post in Westbourne Medical Centre saves a GP 80 hours a month. 1 Researchers in Dudley determined that 769.6 GP hours were saved by 5.4 full-time equivalent pharmacists over 4 months between September and December 2015. 2 This equates to one post saving a GP 35.6 hours per month. 2 We estimated the potential time saved for GPs by tasks being undertaken by part-time pharmacists in three general practices in Canberra, Australia, at 23% from May to December 2017. Assuming that a full-time pharmacist works 37.5 hours per week, our data suggest that 37.4 hours per month of GP time may be saved by one full-time pharmacist.

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)0.001
metaresearch head score (Gemma)0.002
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.039
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.029
GPT teacher head0.331
Teacher spread0.302 · 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 designNot applicable
Domainnot available
GenreCommentary

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
Published2018
Admission routes1
Has abstractyes

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