Evidence map›Paper›PMID 40144605›Full record

ArticleIndian journal of thoracic and cardiovascular surgery2025

Regression analysis for cardiothoracic surgeons: part 5-predicting risks and outcomes in surgery.

H Shafeeq Ahmed

Abstract read
In one paragraph

Article in Indian journal of thoracic and cardiovascular surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

H Shafeeq AhmedBangalore Medical College and Research Institute, K.R Road, Bangalore, 560002 Karnataka India.ORCID 0000-0003-1671-8474

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Regression analysis is a powerful tool for cardiothoracic surgeons, enabling the prediction of patient outcomes and identification of key risk factors. This article explores the fundamentals of regression, including simple, multiple, and logistic regression models, and their applications in predicting surgical success, recovery times, and complication risks. Ensuring model assumptions are valid, selecting appropriate variables, and interpreting coefficients allows clinicians to make evidence-based decisions. Advanced techniques, such as handling non-linear relationships through polynomial regression and log transformations, are essential when dealing with more complex data. These techniques are critical in clinical research for addressing key research questions. Supplementary Information: The online version contains supplementary material available at 10.1007/s12055-025-01901-2.

Indexed as

Data interpretationOutcome assessmentPatient-specific modelingPredictive value of testsRisk assessment

Identifiers

PMID40144605
PMCPMC11933567

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.