ArticleJournal of clinical monitoring and computing2019
A retrospective evaluation of the risk of bias in perioperative temperature metrics.
Article in Journal of clinical monitoring and computing, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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
2 citing papers in PubMed.
- Metrics to assess the quality of anesthesia, perioperative care, and acute pain management in Canada: a scoping review.Canadian journal of anaesthesia = Journal canadien d'anesthesie · 2025Article
- Zero-Heat-Flux and Esophageal Temperature Monitoring in Orthopedic Surgery: An Observational Study.Journal of multidisciplinary healthcare · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
The prevention and treatment of hypothermia is an important part of routine anesthesia care. Avoidance of perioperative hypothermia was introduced as a quality metric in 2010. We sought to assess the integrity of the perioperative hypothermia metric in routine care at a single large center. Perioperative temperatures from all anesthetics of at least 60 min duration between January 2012 and 2017 were eligible for inclusion in analysis. Temperatures were displayed graphically, assessed for normality, and analyzed using paired comparisons. Automatically-recorded temperatures were obtained from several monitoring sites. Provider-entered temperatures were non-normally distributed, exhibiting peaks at temperatures at multiples of 0.5 °C. Automatically-acquired temperatures, on the other hand, were more normally distributed, demonstrating smoother curves without peaks at multiples of 0.5 °C. Automatically-acquired median temperature was highest, 36.8 °C (SD = 0.8 °C), followed by the three manually acquired temperatures (nurse-documented postoperative temperature, 36.5 °C [SD = 0.6 °C]; intraoperative manual temperature, 36.5 °C [SD = 0.6 °C]; provider-documented postoperative temperature, 36.1 °C [SD = 0.6 °C]). Provider-entered temperatures exhibit values that are unlikely to represent a normal probability distribution around a central physiologic value. Manually-entered perioperative temperatures appear to cluster around salient anchoring values, either deliberately, or as an unintended result driven by cognitive bias. Automatically-acquired temperatures may be superior for quality metric purposes.
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Registered trials
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