Cost-Benefit Analysis of Internet Therapeutic Intervention on Patients With Diabetes
Bibliographic record
Abstract
BACKGROUND: With the emergence of IBGMS for allowing for patients to communicate their self-monitored blood glucose (SMBG) readings with their health care providers, their impact on the management of diabetes is becoming well-supported with regards to clinical benefits. Their impact on healthcare costs, however, has yet to be investigated. This study aims to determine the cost-benefits of such interventions in comparison to routine care. OBJECTIVES: To analyze the cost-benefit of an Internet Blood Glucose Monitoring Service (IBGMS) in comparison to routine diabetes care. PATIENTS AND METHODS: 200 patients were surveyed to assess the cost associated with doctor appointments in the past 12 months. Annual number of visits to medical services for diabetes and costs of transportation, parking, and time taken off work for visits were surveyed. Self-reported frequency of SMBG and most recent A1C were also surveyed. We compared 100 patients who used the IBGMS with 100 patients who only used routine care. RESULTS: There is a trend of lowered total cost in the intervention group compared to the control group. The control group spent $210.89 per year on visits to physicians; the intervention group spent $131.26 (P = 0.128). Patients in control group visited their endocrinologist 1.76 times per year, those in intervention group visited their endocrinologist 1.36 times per year, significantly less frequently than the control group (P = 0.014). Number of visits to other medical services is similar between the groups. Average A1C in intervention group is 7.57%, in control group is 7.69% (P = 0.309). CONCLUSIONS: We have demonstrated that IBGMS, while not reaching statistical significance, may be associated with slightly reduced A1C and cost due to visiting physicians.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".