Evidence map›Paper›PMID 29527132›Full record

ReviewCurrent anesthesiology reports2018

Risk Assessment.

Pragya Ajitsaria, Sabry Z Eissa, Ross K Kerridge

Abstract readReview
In one paragraph

Review in Current anesthesiology reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
–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

5 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Article
  4. Article
  5. Article
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

3 authors.

Pragya Ajitsaria1Department of Anaesthesia & Perioperative Medicine, John Hunter Hospital, Locked Bag 1 HRMC, Newcastle, NSW 2310 Australia.
Sabry Z Eissa1Department of Anaesthesia & Perioperative Medicine, John Hunter Hospital, Locked Bag 1 HRMC, Newcastle, NSW 2310 Australia.
Ross K Kerridge1Department of Anaesthesia & Perioperative Medicine, John Hunter Hospital, Locked Bag 1 HRMC, Newcastle, NSW 2310 Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purpose of reviewThe central question of preoperative assessment is not "What can be done?" but "What should be done and how?" Predicting a patient's risk of unwanted outcomes is vital to answering this question. This review discusses risk prediction tools currently available and anticipates future developments. RECENT

findingsSimple, parsimonious risk scales and scores are being replaced by complex risk prediction models as high-capacity information systems become ubiquitous. The accuracy of risk estimation will be further increased by improved assessment of physical fitness, frailty, and incorporation of existing and novel biomarkers. However, the limitations of risk prediction for individual patient care must be recognized. SUMMARY: Risk prediction is transforming from clinical estimation to statistical science. Predictions should be used within the context of a patient's baseline risk (life expectancy independent of surgery), personal circumstances, quality of life, their expectations and values, and consideration of outcomes that are meaningful for the patient.

Indexed as

PrehabilitationPreoperative evaluationPreoperative risk assessmentSurgical risk assessment

Identifiers

PMID29527132
PMCPMC5834592

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.