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Created January 24, 2023, last updated about 1 year ago.
Collection: 158, Score: 343, Trend score: 0, Read count: 528, Articles count: 9, Created: 2023-01-24 00:25:46 UTC. Updated: 2023-01-25 02:44:30 UTC.Notes
The pressure to practice truly patient-focused, evidence-based medicine weighs on every anaesthetist and anaesthesiologist. Yet as the volume of evidence has grown, so has the expectation to always provide the highest quality care.
There is a trap of unknown knowns: evidence known in the greater medical-knowledge body but that we are naively ignorant of.
Bastardising William Gibson (1993), we risk that the evidence:
“…is already here – it's just not very evenly distributed.”
The greatest challenge for evidence-based anaesthesia continues to be the translation of research findings into actual practice change. The key to this is the intersection between quality, personal relevance, general significance, and credibility. But how can we achieve this?
Daniel Jolley
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Collected Articles
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"It usually comes as a surprise to students to learn that some (perhaps most) published articles belong in the bin, and should certainly not be used to inform practice." – Greenhalgh.
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Ioannidis demonstrated that 80% of non-randomized studies were wrong, and among randomized controlled studies 25% were incorrect. Even large, multicenter, randomized clinical trials were predictably wrong in 10% of studies.
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Review Meta Analysis
Trends and predictors of biomedical research quality, 1990-2015: a meta-research study.
To measure the frequency of adequate methods, inadequate methods and poor reporting in published randomised controlled trials (RCTs) and test potential factors associated with adequacy of methods and reporting. ⋯ Even though reporting has improved since 1990, the proportion of RCTs using inadequate methods is high (59.3%) and increasing, potentially slowing progress and contributing to the reproducibility crisis. Stronger incentives for the use of adequate methods are needed.
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To explore the future contradiction of highly-cited research Ioannidis investigated just under 50 of the most significant and highly regarded medical research findings from 1990 to 2003. Of 45 that concluded their interventions were effective, 34 had had their hypothesis retested. Of these 34, over 40% (14) were subsequently shown to be incorrect or exaggerated. Forty percent of some of the most highly regarded, practice-changing medical evidence from the 20th century subsequently disproven!
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Tatsioni found that earlier disproven observational studies were still positively cited in 50% or more of peer reviewed publications, despite the existence of well-established contrary evidence.
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Journal of medical ethics · Jul 2022
ReviewFraud and retraction in perioperative medicine publications: what we learned and what can be implemented to prevent future recurrence.
90% of fraudulent papers in perioperative medicine retracted over the last 30 years were authored by only six researchers.
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50% of retracted anesthesiology papers are retracted because of fraud, and 30% because of inadequate ethics approval.
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Carlisle investigated the distribution of independent variables between study groups in Fujii's fraudulent research:
summary"The published distributions of 28/33 variables (85%) were inconsistent with the expected distributions, such that the likelihood of their occurring ranged from 1 in 25 to less than 1 in 1 000 000 000 000 000 000 000 000 000 000 000 (1 in 1033), equivalent to p values of 0.04 to < 1 × 10-33 , respectively."
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A focus on novel, confirmatory, and statistically significant results leads to substantial bias in the scientific literature. One type of bias, known as "p-hacking," occurs when researchers collect or select data or statistical analyses until nonsignificant results become significant. ⋯ We then illustrate how one can test for p-hacking when performing a meta-analysis and show that, while p-hacking is probably common, its effect seems to be weak relative to the real effect sizes being measured. This result suggests that p-hacking probably does not drastically alter scientific consensuses drawn from meta-analyses.
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