Investigating the Success of Cover Flap in Patients with Bedsore
Bibliographic record
Abstract
Objective: The high prevalence of bedsore creates many problems for the maintenance system and the patients, and in addition to spending high costs for treating ulcers, a lot of time is also dedicated to caring for them. The purpose of performing this study is to investigate the therapeutic results of recovering bedsore injuries by cover flaps. Work Method: This study is of prospective type in which 85 patients with bedsore who had referred to Taleghani Hospital in Kermanshah for treatment by muscle cover flaps during the years of 2016 to 2017, were followed up at time periods of 1 week, and 3 months after the discharge and in case of failure, they were recovered. Finally, the obtained data were analyzed by using statistical tests and SPSS version 22 software. Results: The obtained results showed that the success percentage of recovering bedsores was significantly increased after one week, and 3 months by cover flaps (P <0.05). Also, the success percentage of recovering bedsores by cover flap after one week, and 3 months in terms of age, gender, and BMI of patients significantly shows an increase (P <0.05). Conclusion: In general, it can be concluded from this study that using cover flaps leads to the success of recovering bedsores after 3 months of treatment, and the age, gender, and BMI variables of patients cannot be effective in this improvement process.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".