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Record W4308374264 · doi:10.1016/s2213-8587(22)00278-9

The impact of the COVID-19 pandemic on diabetes services: planning for a global recovery

2022· review· en· W4308374264 on OpenAlexaff
Kamlesh Khunti, Vanita R. Aroda, Pablo Aschner, Juliana C.N. Chan, Stefano Del Prato, Clare Hambling, Stewart B. Harris, Roberta Lamptey, Martin McKee, Nikhil Tandon, Jonathan Valabhji, Samuel Seidu

Bibliographic record

VenueThe Lancet Diabetes & Endocrinology · 2022
Typereview
Languageen
FieldMedicine
TopicCOVID-19 and healthcare impacts
Canadian institutionsWestern University
FundersNIHR Leicester Biomedical Research CentreNational Institute for Health and Care Research
KeywordsMedicinePandemicDiabetes mellitusPsychological interventionPublic healthDiseaseType 2 diabetesIntensive care medicineDiabetes managementEnvironmental healthGerontologyCoronavirus disease 2019 (COVID-19)Infectious disease (medical specialty)PsychiatryInternal medicineNursing

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.005
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.019
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0030.003
Science and technology studies0.0010.001
Scholarly communication0.0030.004
Open science0.0020.004
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0160.004

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 head0.228
GPT teacher head0.478
Teacher spread0.250 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
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

Citations107
Published2022
Admission routes1
Has abstractno

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