Bibliographic record
Abstract
BACKGROUND: Diabetic patients can develop spontaneous infarctions of muscle. The optimal treatment of this diabetic muscle infarction (DMI) is unknown. OBJECTIVE: This analysis was conducted to compare the outcomes of conservative, medical, and surgical treatments of DMI. The primary outcome is the time to recovery. Secondary outcomes include recurrence and mortality rates. METHODS: A MEDLINE search from its inception to December 2002 was used to identify reported cases of DMI. We selected those cases that reported on specified baseline characteristics of the patients, including age, gender, duration of diabetes, type of diabetes, diabetic microvascular and macrovascular complications, and the magnetic resonance imaging or computed tomography findings, the type of therapy provided, the time to recovery of initial muscle infarction, recurrences, and deaths. RESULTS: A total of 36 references meeting our inclusion criteria were retrieved, describing 49 patients. Thirty-four patients received conservative therapy (bedrest and analgesics), 8 received medical therapy (antiplatelet agents and/or steroids), and 7 had surgical excision of the infarcted muscle. There were no significant differences in baseline characteristics. The time to recovery from treatment onset was 8.1 weeks, 5.5 weeks, and 13 weeks in the conservative, medical, and surgical treatment groups, respectively. This was statistically significant only when comparing medical and surgical treatment. The respective recurrence rates were 35%, 29%, and 71%. The respective mortality rates within 2 years were 4%, 14%, and 29%. CONCLUSION: This study supports the use of nonsurgical treatment in patients with DMI. It also demonstrates that DMI can be temporally associated with death.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".