ReviewMalaysian family physician : the official journal of the Academy of Family Physicians of Malaysia2026
Narrative review of the development of an ischaemic heart disease prognostic scoring tool (i-IHD score) among patients with type 2 diabetes mellitus in Malaysia.
Review in Malaysian family physician : the official journal of the Academy of Family Physicians of Malaysia, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Ischaemic heart disease (IHD) remains a major cause of mortality among individuals with type 2 diabetes mellitus (T2DM) in Malaysia. Conventional cardiovascular risk models, such as the Framingham risk score, often show limited calibration in Asian populations. Artificial intelligence (Al)-calibrated models have emerged as potential alternatives, yet their generalisability and clinical utility across different populations remain uncertain. This narrative review aimed to summarise existing prognostic models for IHD in patients with T2DM and identify methodological gaps relevant to the development of a locally calibrated model. Methods: This narrative review employed a structured search strategy guided by PRISMA principles but was not conducted as a full systematic review. We synthesised evidence from epidemiological and prognostic research. Studies comparing conventional statistical approaches (e.g. logistic regression and Cox models) with AI-calibrated models such as extreme gradient boosting, random forest and support vector machines were reviewed. Results: Eleven studies met the inclusion criteria; four used conventional statistical methods, and seven applied AI or machine learning algorithms. The reported discrimination (area under the curve=0.66-0.94) varied widely. Conventional models commonly lacked external validation and demonstrated restricted applicability beyond their original cohorts. AI-calibrated models showed promising discrimination in some datasets but similarly experienced limited validation and lacked benchmarking against traditional statistical methods. Across the studies, limited calibration and validation reduced generalisability to heterogeneous Malaysian populations. Conclusion: Developing a locally AI-calibrated i-IHD score could enable early risk identification, guide targeted interventions and support national health initiatives, including the Health White Paper 2023 and 13th Malaysia Plan.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.