Assessment of the Consistency of Tetabulin Injection to the Patients with an Open Fracture Referred to the Khatamolanbia Hospital, Zahedan in 2017 with the National Guidelines
Bibliographic record
Abstract
Background and Goal: Open fractures are at risk of infection with Clostridium tetani and severe traumatic infections. Tetabulin injection is strongly recommended for the patients with an open fracture and severe wounds. The goal of this study is to assess the consistency of tetabulin injection to the patients with an open fracture referred to the Khatamolanbia hospital in Zahedan in 2017 with the national guidelines. Materials and Methods: This study is a cross-sectional descriptive study. 300 patients with an open fracture referred to the ER of the Khatamolanbia Hospital in Zahedan in 2017 were selected as the sample. Their fracture type and severity were assessed. The data were classified in the tables and statistically analyzed using Chi-square, pared t-test, Pearson correlation, and regression in SPSS 26. Findings: Among 300 patients, 275 patients (91.7%) were male and 25 patients (8.3%) were female. The most frequent age range was 20 to 30 years old (31.7%), and the least frequent ones were 5 to 10 years old (10%) and more than 50 years old (11.6%). The results showed that gender has no significant effect on the predictability of the need of tetabulin injection for the patients with open fractures (P=0.780). However, age has a significant positive effect on the predictability of the need of tetabulin injection for the patients with open fractures; as the age increases, the need for tetabulin injection also increases, and it must be injected in the 50 years and older patients (P=0.05). Conclusion: The results showed that age was effective on the decrease of the serum level of anti-tetanus antibody, however, gender had no significant effect on it. Therefore, it is concluded that tetabulin injection for open fractures is consistent with the national guideline.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".