Sensor-augmented insulin pump more effective than multiple daily insulin injections for reducing HbA1C in people with poorly controlled type 1 diabetes
Bibliographic record
Abstract
Commentary on: 1. Bergenstal RM, 2. Tamborlane WV, 3. Ahmann A, 4. et al .; STAR 3 Study Group. Effectiveness of sensor-augmented insulin-pump therapy in type 1 diabetes. N Engl J Med 2010;363:311–20. [OpenUrl][1][CrossRef][2][PubMed][3] With developing technologies, continuous subcutaneous insulin infusion systems (CSII) and continuous subcutaneous glucose monitors (CGM) have become available to treat diabetes mellitus (DM). The effectiveness of CSII to lower HbA1c has been shown, especially in poorly regulated patients with type 1 DM.1 More recent trials investigated the application of CGM in type 1 DM and proved that CGM is effective in lowering HbA1c, again especially in poorly regulated adult patients, provided that they were compliant and tolerated the device.2 Now the two devices have been integrated, augmenting the insulin pump with the sensor. The efficacy of this new treatment platform was investigated. This was an unblinded randomised controlled trial, performed in 30 diabetes centres throughout the USA and Canada, comparing the efficacy of 1-year sensor-augmented pump therapy (Minimed Paradigm REAL-time system; Medtronic, Northridge, CA.) to multiple daily insulin injection therapy in 485 patients with type 1 DM. Eligible patients were aged 7–70 years old, were on multiple daily injection therapy for at least 3 months prior to inclusion and had HbA1c between 7.4% and 9.5%. Patients who had used insulin pump therapy within 3 years prior to the … [1]: {openurl}?query=rft.jtitle%253DNew%2BEngland%2BJournal%2Bof%2BMedicine%26rft.stitle%253DNEJM%26rft.issn%253D0028-4793%26rft.volume%253D363%26rft.issue%253D4%26rft.spage%253D311%26rft.epage%253D320%26rft.atitle%253DEffectiveness%2Bof%2Bsensor-augmented%2Binsulin-pump%2Btherapy%2Bin%2Btype%2B1%2Bdiabetes.%26rft_id%253Dinfo%253Adoi%252F10.1056%252FNEJMoa1002853%26rft_id%253Dinfo%253Apmid%252F20587585%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [2]: /lookup/external-ref?access_num=10.1056/NEJMoa1002853&link_type=DOI [3]: /lookup/external-ref?access_num=20587585&link_type=MED&atom=%2Febmed%2F16%2F2%2F46.atom
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
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".