Evidence map›Paper›PMID 24163259›Full record

ReviewGeneral thoracic and cardiovascular surgery2013

Cardiac surgical patients are not the same. But who knows that: the patient, the cardiologist or the surgeon?

Haralabos Parissis, Bassel Al-Alao

Abstract readReview
PubMed Publisher
In one paragraph

Review in General thoracic and cardiovascular surgery, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

2 authors.

Haralabos ParissisDepartment of Cardiothoracic Surgery, Royal Victoria Hospital, Grosvenor Road, Belfast, BT12 6BA, UK, hparissis@yahoo.co.uk.
Bassel Al-Alao

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Nowadays, outcome measures get published following risk adjustment; mortality rates are adjusted for risks to give a picture of the performance of a surgeon or a unit. The risk factors associated with in-hospital death are likely to reflect concurrent disease-specific variables. Factors associated with increased resource utilisation (as measured by length of stay) reflect serious co-morbid illness. The various models are effective at predicting population behaviour, but not necessarily suited for predicting individual outcomes. Further work needs to be done both to explain the differences in risk factors seen between the various risk stratification models and to determine which models are best suited in clinical practice. This document reviews the current evidence and risk stratification tools currently employed in cardiac surgery practice.

Indexed as

Cardiac Surgical ProceduresComorbidityHeart DiseasesHumansModels, TheoreticalOutcome Assessment, Health CarePrognosisRisk AssessmentRisk FactorsTreatment Outcome

Identifiers

What OpenQuestion 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 OpenQuestion graph.