MétaCan
Menu
Back to cohort
Record W2157368123 · doi:10.3810/psm.2011.05.1896

Clinical Management of the Physically Active Patient with Type 1 Diabetes

2011· review· en· W2157368123 on OpenAlexaff
Lisa Chu, Jill Hamilton, Michael C. Riddell

Bibliographic record

VenueThe Physician and Sportsmedicine · 2011
Typereview
Languageen
FieldMedicine
TopicDiabetes Management and Research
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of TorontoYork University
Fundersnot available
KeywordsMedicineType 2 diabetesDiabetes mellitusGlycemicHypoglycemiaDiabetes managementType 1 diabetesIntensive care medicineInsulinPsychosocialInsulin pumpInternal medicineEndocrinology

Abstract

fetched live from OpenAlex

The prevalence and incidence of type 1 diabetes continues to increase worldwide. Most patients with type 1 diabetes are young at the time of diagnosis and wish to continue leading a physically active life. Although regular exercise, insulin therapy, and proper nutrition are the cornerstone of treatment, there are considerable challenges in managing the active individual with type 1 diabetes. The current recommendation for diabetes management is intensive glycemic control for all patients when possible to help prevent secondary complications. Both insulin pump therapy and multiple daily injections are beneficial treatment options to lower average glucose levels; however, without continuous glucose monitoring, these treatment options typically increase the risk of hypoglycemia. In active patients with type 1 diabetes, the challenges of maintaining good glycemia are complicated by the inability to regulate insulin concentrations during and after exercise. Physiological and psychosocial factors during growth and maturation also provide additional challenges. This article highlights challenges and key strategies for diabetes management in the active individual with type 1 diabetes, including the application of the most recent diabetes technologies.

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)0.000
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: Review · Consensus signal: Review
Teacher disagreement score0.989
Threshold uncertainty score0.502

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.052
GPT teacher head0.351
Teacher spread0.299 · 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
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

Citations52
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueThe Physician and SportsmedicineSame topicDiabetes Management and ResearchFrench-language works237,207