Efficacy and tolerance of intranasal insulin administered during 4 months in severely hyperglycaemic Type 2 diabetic patients with oral drug failure: a cross‐over study
Bibliographic record
Abstract
AIMS: We have evaluated the local tolerance and the metabolic efficacy of a lyophilized nasal insulin preparation in 10 severely hyperglycaemic Type 2 diabetic patients. METHODS: The study included two 4-month randomized periods: (A) three preprandial doses of nasal insulin secondarily combined with one evening subcutaneous NPH if the desired glycaemic control was not achieved; (B) two NPH injections daily. We assessed: (i) diabetes control on monthly HbA1c levels and occurrence of hypoglycaemic events; (ii) local tolerance on clinical symptoms, rhinoscopy, nasal muco-ciliary clearance and nasal biopsies; (iii) insulin absorption at months 0 and 4. RESULTS: One patient was withdrawn because of cough and dizziness after each nasal application. HbA1c was not significantly different at month 4 (9.4 +/- 0.5% vs. 8.8 +/- 0.2%, A vs. B). Blood glucose control remained only fair in the majority of our patients. Nasal insulin was able to replace the daytime fraction of the subcutaneous insulin with a 18% efficacy. Side-effects included transient nasal hyperactivity (pruritus, sneezing and rhinorrhoea) and chronic persistence of nasal crusts. Plasma insulin profiles were not significantly different between months 0 and 4. CONCLUSIONS: The utilization of nasal insulin (with or without NPH) was associated with similar diabetes control compared with NPH twice daily. Nasal insulin alone was able to achieve an adequate glycaemic control in three of the 10 patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".