Accuracy of Peripheral Thermometers for Estimating Temperature
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Bibliographic record
Abstract
Letters5 July 2016Accuracy of Peripheral Thermometers for Estimating TemperatureDaniel J. Niven, MD, MSc, PhD, Kevin B. Laupland, MD, MSc, and Henry Thomas Stelfox, MD, PhDDaniel J. Niven, MD, MSc, PhDFrom University of Calgary, Calgary, Alberta, Canada, and Royal Inland Hospital, Kamloops, British Columbia, Canada., Kevin B. Laupland, MD, MScFrom University of Calgary, Calgary, Alberta, Canada, and Royal Inland Hospital, Kamloops, British Columbia, Canada., and Henry Thomas Stelfox, MD, PhDFrom University of Calgary, Calgary, Alberta, Canada, and Royal Inland Hospital, Kamloops, British Columbia, Canada.Author, Article, and Disclosure Informationhttps://doi.org/10.7326/L16-0069 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail IN RESPONSE:Dr. Bonzi and colleagues raise 2 questions about our meta-analysis examining the accuracy of peripheral thermometers. A fundamentally important concern is the effect of interstudy heterogeneity on the clinical applicability of our pooled analyses. Although this heterogeneity was the main limitation of our meta-analysis, we believe that our pooled analyses remain clinically applicable.First, data were pooled across all studies, because our main objective was to describe peripheral thermometer accuracy. Second, we recognize that clinicians caring for different populations of patients benefit from data specific to a population or thermometer; therefore, we conducted prespecified subgroup analyses to examine ...References1. Leeflang MM, Deeks JJ, Gatsonis C, Bossuyt PM; Cochrane Diagnostic Test Accuracy Working Group.. Systematic reviews of diagnostic test accuracy. Ann Intern Med. 2008;149:889-97. [PMID: 19075208] LinkGoogle Scholar2. Kurz A, Sessler DI, Lenhardt R. Perioperative normothermia to reduce the incidence of surgical-wound infection and shorten hospitalization. Study of Wound Infection and Temperature Group. N Engl J Med. 1996;334:1209-15. [PMID: 8606715] CrossrefMedlineGoogle Scholar3. Young PJ, Saxena M, Beasley R, Bellomo R, Bailey M, Pilcher D, et al. Early peak temperature and mortality in critically ill patients with or without infection. Intensive Care Med. 2012. [PMID: 22290072] CrossrefMedlineGoogle Scholar4. Saxena M, Young P, Pilcher D, Bailey M, Harrison D, Bellomo R, et al. Early temperature and mortality in critically ill patients with acute neurological diseases: trauma and stroke differ from infection. Intensive Care Med. 2015;41:823-32. [PMID: 25643903] doi:10.1007/s00134-015-3676-6 CrossrefMedlineGoogle Scholar Author, Article, and Disclosure InformationAffiliations: From University of Calgary, Calgary, Alberta, Canada, and Royal Inland Hospital, Kamloops, British Columbia, Canada.Disclosures: Authors have disclosed no conflicts of interest. Forms can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M15-1150. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetailsSee AlsoAccuracy of Peripheral Thermometers for Estimating Temperature Daniel J. Niven , Jonathan E. Gaudet , Kevin B. Laupland , Kelly J. Mrklas , Derek J. Roberts , and Henry Thomas Stelfox Accuracy of Peripheral Thermometers for Estimating Temperature Mattia Bonzi , Elisa Maria Fiorelli , Monica Solbiati , Nicola Montano , and Metrics Cited byPediatric Vital Sign Distribution Derived From a Multi-Centered Emergency Department Database 5 July 2016Volume 165, Issue 1Page: 73-74KeywordsBrainFactor analysisFeversHypothermiaPatientsPopulation statisticsRandomized trialsSafetyTemperatureThermometers ePublished: 5 July 2016 Issue Published: 5 July 2016 Copyright & PermissionsCopyright © 2016 by American College of Physicians. All Rights Reserved.PDF downloadLoading ...
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it