Notes

Urinary peptidomic single profiles to address age-related afflictions: a prospective population research.
Diagnostic Level III. See Instructions for Authors for a complete description of levels of evidence.
Diagnostic Level III. See Instructions for Authors for a complete description of levels of evidence.
To evaluate the outcomes of patients who underwent soft tissue flap coverage during treatment of a tibia fracture nonunion.

Retrospective analysis on prospectively collected data.

Academic medical center.

157 patients were treated for a fracture nonunion following a tibia fracture over a 15-year period. Sixty-six had sustained an open tibial fracture initially and 25 of these patients underwent soft tissue flaps for their open tibia fracture nonunion.

Manipulation of soft tissue flaps, either placement or elevation for graft placement in ununited previously open tibial fractures.

Bony healing, time to union, ultimate soft tissue status, postoperative complications, and functional outcome scores using the Short Musculoskeletal Functional Assessment (SMFA). This group was compared to a group of open tibial fracture nonunions that did not undergo soft tissue transfer.

Bony healing was achieved in 24/25 patients (96.0%) who received flaps at a mean time to union of 8.7 ± 3.3 months compared to 39/41 patients (95.1%) at a mean 7.5 ± 3.2 months (p > 0.05) in the non-coverage group. Healing rate and time to union did not differ between groups. At latest follow-up, the flap coverage group reported a mean SMFA index of 17.1 compared to an SMFA index of 27.7 for the non-coverage group (p = 0.037).

Utilization of soft tissue flaps in the setting of open tibia shaft nonunion repair surgery are associated with a high union rate (>90%). Coverage with or manipulation of soft tissue flaps did not result in improved bony healing rate or time to union compared to those who did not require flaps. However, soft tissue flap coverage was associated with higher functional scores at long-term follow-up.

Therapeutic Level III. See Instructions for Authors for a complete description of levels of evidence.
Therapeutic Level III. See Instructions for Authors for a complete description of levels of evidence.
To determine if inpatient counseling with additional counseling could increase smoking cessation.

Prospective study.

Level I trauma center.

Current smokers with an operative fracture randomly assigned to control (no counseling), brief counseling (inpatient counseling), or intense counseling (brief counseling plus follow-up) groups.

Brief inpatient smoking counseling and referral to a nationally-based quitline.

Smoking cessation confirmed by exhaled carbon monoxide, recorded at 12 and 26 weeks.

Overall, 266 patients participated, with 40, 111, and 115 patients in the control and treatment groups, respectively. At 3 months, 17% of control versus 11% and 10% brief and intense counseling groups quit smoking, respectively. At 6 months, 15% of control, and 10% and 5% of the respective counseling groups quit. No significant difference reported between groups. Forty-three percent of patients accepted quitline referral. Intense counseling patients were 3 times more likely to accept referral (odds ratio [OR], 3.1; 95% confidence interval [CI], 1.4-6.9) and brief counseling patients were more than 2 times as likely to accept referral (OR, 2.3; 95% CI, 1.0-5.1). Overall, 54% of participants who accepted the quitline referral accepted quitline services. Intense counseling (OR, 8.2; 95% CI, 1.0-68.5) and brief counseling (OR, 5.3; 95% CI, 0.6-44.9) patients were more likely to use quitline services.

Increasing levels of inpatient counseling can improve successful referral to quitline, but it does not appear to influence quit rates amongst orthopaedic trauma patients.

Therapeutic Level I. buy JNJ-26481585 See Instructions for Authors for a complete description of levels of evidence.
Therapeutic Level I. See Instructions for Authors for a complete description of levels of evidence.
The use of billing codes (ICD-10) to identify and track cases of gestational and pregestational diabetes during pregnancy is common in clinical quality improvement, research, and surveillance. However, specific diagnoses may be misclassified using ICD-10 codes, potentially biasing estimates. The goal of this study is to provide estimates of validation parameters (sensitivity, specificity, positive predictive value, and negative predictive value) for pregestational and gestational diabetes diagnosis using ICD-10 diagnosis codes compared with medical record abstraction at a large public hospital in Atlanta, Georgia.

This study includes 3,654 deliveries to Emory physicians at Grady Memorial Hospital in Atlanta, Georgia, between 2016 and 2018. We linked information abstracted from the medical record to ICD-10 diagnosis codes for gestational and pregestational diabetes during the delivery hospitalization. Using the medical record as the gold standard, we calculated sensitivity, specificity, positive predictive value, and negative predictive value for each.

For both pregestational and gestational diabetes, ICD-10 codes had a high-negative predictive value (>99%, Table 3) and specificity (>99%). For pregestational diabetes, the sensitivity was 85.9% (95% CI = 78.8, 93.0) and positive predictive value 90.8% (95% CI = 85, 97). For gestational diabetes, the sensitivity was 95% (95% CI = 92, 98) and positive predictive value 86% (95% CI = 81, 90).

In a large public hospital, ICD-10 codes accurately identified cases of pregestational and gestational diabetes with low numbers of false positives.
In a large public hospital, ICD-10 codes accurately identified cases of pregestational and gestational diabetes with low numbers of false positives.
Social stratification is a well-documented determinant of mental health. Traditional measures of stratification (e.g., socioeconomic status) reduce dynamic social processes to individual attributes downstream of mechanisms that generate stratification. In this study, we measure one process theorized to generate and reproduce social stratification-economic exploitation-and explore its association with mental health.

Data are from the 1983 to 2017 waves of the Panel Study of Income Dynamics, a nationally representative cohort study (baseline N = 3059). We operationalized "unconcealed exploitation" as the percentage of individuals' labor income they were hypothetically not paid for productive hours. We ascertained psychologic distress and mental illness with the Kessler-6 (K6) scale.

We fit inverse probability-weighted marginal structural models and found that for each unit increase in unconcealed exploitation, psychologic distress increased by 1.6 points (95% confidence interval = 0.71, 2.5) on the K6 scale and the odds of mental illness tripled (odds ratio = 3.
Here's my website: https://www.selleckchem.com/products/JNJ-26481585.html