398-P: Hypoglycemia Requiring Paramedic Assistance in London, Canada
Bibliographic record
Abstract
Hypoglycemia requiring paramedic assistance places a large burden on the health care system (HCS), and negatively impacts quality of life and long-term outcomes in patients with diabetes. Prior studies have shown that only a small proportion of patients with paramedic-assist requiring hypoglycemia are subsequently brought to the Emergency Room; thus severe hypoglycemia (SHG) is often “invisible” to the HCS and is not adequately followed up on. We created an innovative, two-step hypoglycemia education program in London, Canada which consists of a direct electronic referral system by paramedics at the time they assess patients with SHG to the local Diabetes Education Center (DEC) for a standardized hypoglycemia education. There were a total of 446 SHG calls to the Middlesex-London Paramedic Service from September 2017- June 2018. Of those, 322 (72%) were eligible for our study, of whom 135 (42%) were referred to the DEC and only 79 patients agreed to attend. Of the 79 patients who were enrolled over 21 months, 49 (62%) were male; the mean age was 61±16 (range 18-88 yrs); and 40 (51%) had type 2, 32 (41%) type 1, and 7 (9%) were unknown diabetes type. Of those 48 (61%) were taking insulin, 8 (10%) oral hypoglycemic agents of which the most common was glyburide (5/8, 63%), and 19 (24%) were on both oral agents and insulin. Three (4%) patients were on an insulin pump. Mean HbA1c was 63±9 mmol/mol (7.9±1.3, range 4.8-10.6%) and the majority did not have an Endocrinologist (49, 62%). Only 50 (63%) completed the education program. There were no statistically significant differences between the characteristics of the patients who attended and did not attend DEC program. Patients experiencing SHG remain ’hidden’ from the HCS. Hence, the impact of SHG is underestimated and understudied. Despite an accessible referral system to a focused intervention, our program was not successful in engaging many patients with SHG requiring paramedic assistance in a program to decrease their risk for SHG. Further study to explore barriers is needed. Disclosure T. Spaic: Research Support; Self; Novo Nordisk Inc. Speaker’s Bureau; Self; Dexcom, Inc., Sanofi. S.L. Liu: Consultant; Self; Merck & Co., Inc., Novo Nordisk Inc., Sanofi. Research Support; Self; Novo Nordisk Inc., Sanofi. D.R. Carter: None. M. Peddle: None. J. Mahon: None. Funding Academic Medical Organization of Southwestern Ontario
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".