Publications by authors named "M K Heng"

To address the limitations of carbon nitride in photocatalysis, we propose constructing a three-dimensional interwoven SiC/g-CN composite structure. Utilizing the strong microwave-thermal conversion characteristics of SiC whiskers, localized "hot spots" are generated, which induce rapid thermal gradients, promoting rapid polymerization of urea and in situ formation of the interwoven network. This unique structure strengthens the interaction between these two components, creates multiple electron transport pathways, enhances CO adsorption, and effectively improves charge separation while reducing photogenerated carrier recombination.

View Article and Find Full Text PDF

Purpose: Bookmarking is a qualitative method used to assign descriptive labels to ranges of patient-reported outcome (PROM) scores. We aimed to evaluate variability between bookmarking samples and test score ranges where there was variability in expert opinion in previous studies.

Methods: We conducted two bookmarking sessions with patients who experienced orthopaedic fractures (n = 11) and one session with orthopaedic clinicians (n = 10).

View Article and Find Full Text PDF

Objective: What is the effect of surgical or conservative treatment on the in-hospital outcomes of patients with combined fractures of the clavicle and ribs?

Design: Retrospective cohort study.

Setting: Two level-1 trauma centers and academic teaching hospitals in Boston, Massachusetts.

Patients: All adult patients with a clavicle fracture and ≥3 rib fractures admitted from 2016 to 2021.

View Article and Find Full Text PDF

Purpose: We investigated the validity of the German and Spanish translations of 35 new high functioning items added to the Patient Reported Outcomes Measurement Information System (PROMIS®) Physical Function item bank 2.0. We assessed differential item functioning (DIF) between three general population samples from Argentina, Germany, and the United States.

View Article and Find Full Text PDF

Recent advances in deep learning and natural language processing (NLP) have broadened opportunities for automatic text processing in the medical field. However, the development of models for low-resource languages like French is challenged by limited datasets, often due to legal restrictions. Large-scale training of medical imaging models often requires extracting labels from radiology text reports.

View Article and Find Full Text PDF