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Record W2174702366 · doi:10.1155/2016/1974863

The Need for Improved Management of Painful Diabetic Neuropathy in Primary Care

2016· article· en· W2174702366 on OpenAlexaffabout
Teresa Sobhy

Bibliographic record

VenuePain Research and Management · 2016
Typearticle
Languageen
FieldMedicine
TopicPain Mechanisms and Treatments
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicinePrimary careModalitiesPeripheral neuropathyIntensive care medicineClinical PracticeDiabetes mellitusQuality of life (healthcare)Clinical researchType 2 diabetesFamily medicineNursingInternal medicine

Abstract

fetched live from OpenAlex

The provision of care for patients with type II diabetes in primary care must involve assessing patients for peripheral neuropathy of the feet. Objectives. This paper will demonstrate that painful diabetic neuropathy (PDN) is poorly assessed for and treated in primary care. Methods. A critical analysis of research will be conducted to identify the prevalence and impact of PDN among individuals with type II diabetes. Results. Research evidence and best practice guidelines are widely available in supporting primary care practitioners to better assess for and treat PDN. However, the lack of knowledge, awareness, and implementation of such research and guidelines prevents patients with PDN from receiving appropriate care. Discussion. Much international research exists on the prevalence and impact of PDN in primary care; however, Canadian research is lacking. Furthermore, the quantity and quality of research on treatment modalities for PDN are inadequate. Finally, current research and guidelines on PDN management are inadequately implemented in the clinical setting. Conclusion. The undertreatment of PDN has significant implications on the individual, family, and society. Healthcare practitioners must be more aware of and better implement current research and guidelines into practice to resolve this clinical issue.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.975
Threshold uncertainty score0.234

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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)0.0000.000

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 head0.028
GPT teacher head0.311
Teacher spread0.283 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations8
Published2016
Admission routes2
Has abstractyes

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