Insulin adjustment by a diabetes nurse educator improved glucose control in patients with poorly controlled, “insulin requiring” diabetes
Bibliographic record
Abstract
Thompson DM, Kozak SE, Sheps S. Insulin adjustment by a diabetes nurse educator improves glucose control in insulin-requiring diabetic patients: a randomized trial. CMAJ1999 Oct 19; 161 : 959 –62 [OpenUrl][1][Abstract/FREE Full Text][2] QUESTION: Does regular telephone advice by a diabetes nurse educator for insulin adjustment improve glucose control in patients with poorly controlled, “insulin requiring” diabetes? Randomised (allocation concealed), blinded (outcome assessor), controlled trial with 6 months of follow up. Hospital diabetes clinic in Vancouver, British Columbia, Canada. 46 patients with diabetes (mean age 49 y, 52% women) who had a glycated haemoglobin (HbA1c) levels ≥8.5%, were on insulin therapy, had received standard diabetes education, were able to monitor blood glucose levels at home, and were receiving care by an endocrinologist. Exclusion criteria were inability to have regular telephone communication, … [1]: {openurl}?query=rft.jtitle%253DCanadian%2BMedical%2BAssociation%2BJournal%26rft.stitle%253DCMAJ%26rft.aulast%253DThompson%26rft.auinit1%253DD.%2BM.%26rft.volume%253D161%26rft.issue%253D8%26rft.spage%253D959%26rft.epage%253D962%26rft.atitle%253DInsulin%2Badjustment%2Bby%2Ba%2Bdiabetes%2Bnurse%2Beducator%2Bimproves%2Bglucose%2Bcontrol%2Bin%2Binsulin-requiring%2Bdiabetic%2Bpatients%253A%2Ba%2Brandomized%2Btrial%26rft_id%253Dinfo%253Apmid%252F10551191%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/ijlink?linkType=ABST&journalCode=cmaj&resid=161/8/959&atom=%2Febnurs%2F3%2F2%2F51.atom
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How this classification was reachedexpand
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.000 |
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.
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, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".