Bibliographic record
Abstract
Economic development has lead to increased life expectancy, population growth, and spread of the Western life style, resulting in a gradual increase of diabetic patients during the last three decades. Outcome research focusing on the economics of the medical field began in mid the 1990s, including publications about costs, cost-effectiveness analysis, and policy reflection. According to the ADA, direct cost spending on diabetes was $91.8 billion in 2002 and is projected to be $156 billion in 2010 and $192 billion in 2020. In Canada, research found that the direct cost of diabetic care was $2.6 million (American dollars) in 1998, 7.8% of the total Canadian medical expenditure. Half of this cost was incurred in hospitals (IPD: 19%, medication: 31%). Recent domestic studies have analyzed the expenses associated with type 2 diabetes in some general hospitals. Type 2 diabetic patients, without complication, spend about 1,184,563 won annually on healthcare. On the other hand, patients with microvascular diseases spend up to 4.7 times as much, and patients with macrovascular disease incur up to 10.7 times greater costs. Patients with both complications have been shown to pay 8.8 times more than do those with no complications. The increased costs charged to kidney transplant patients was about 23.1 times greater than for those with no complications, while dialysis increased costs by 21 times, macrovascular disease with PTCA or CABG resulted in a 12.4-fold increase, and BKA was 11.8-fold more expensive. The total medical costs have soared with the treatment progress of diabetic retinopathy or nephropathy. In diabetic treatments, complication occurrence ultimately has an effect on the QOL, the patient mortality, and is associated with the direct medical expenses. Thus it is critical not to delay care in diabetic patients in order to avoid increased direct medical costs. Therefore, in diabetic care, as outlined in the medical care plan policy, it is most critical to adequately control blood sugar, blood pressure, and cholesterol in conjunction with the early discovery of any complications through the appropriate management techniques. (J Korean Diabetes 2011;12:2-5)
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.228 | 0.144 |
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".