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Record W3038673696 · doi:10.1186/s13054-020-02976-6

Significance of body temperature in elderly patients with sepsis

2020· article· en· W3038673696 on OpenAlexaffabout
Takashi Shimazui, Taka‐aki Nakada, Keith R. Walley, Taku Oshima, Toshikazu Abe, Hiroshi Ogura, Atsushi Shiraishi, Shigeki Kushimoto, Toshihiko Mayumi, Takehiko Tarui, Toru Hifumi, Kohji Okamoto, Yutaka Umemura, Yuichiro Sakamoto, Junichi Sasaki, Shin‐ichiro Shiraishi, Ryosuke Tsuruta, Akiyoshi Hagiwara, Kazuma Yamakawa, Tomohiko Masuno, Naoshi Takeyama, Hiroto Ikeda, Masashi Ueyama, Satoshi Fujimi, Satoshi Gando, Daizoh Saitoh, Yasukazu Shiino, Joji Kotani, Norio Yamashita, Osamu Tasaki, Yasumitsu Mizobata, Hiraku Funakoshi, Toshiro Okuyama, Iwao Yamashita, Toshio Kanai, Yasuo Yamada, Mayuki Aibiki, Keiji Sato, Susumu Yamashita, Kenichi Yoshida, Shunji Kasaoka, Akihide Kon, Hiroshi Rinka, Hiroshi Kato, Hiroshi Okudera, Eichi Narimatsu, Toshifumi Fujiwara, Manabu Sugita, Yasuo Shichinohe, Hajime Nakae, Ryouji Iiduka, Mitsunobu Nakamura, Yuji Murata, Yoshitake Sato, Hiroyasu Ishikura, Yasuhiro Myojo, Yasuyuki Tsujita, Kosaku Kinoshita, Hiroyuki Yamaguchi, Toshihiro Sakurai, Satoru Miyatake, Takao Saotome, Susumu Yasuda, Seitaro Fujishima, Kiyotsugu Takuma, Yasuaki Mizushima

Bibliographic record

VenueCritical Care · 2020
Typearticle
Languageen
FieldMedicine
TopicSepsis Diagnosis and Treatment
Canadian institutionsUniversity of British Columbia
FundersKawasaki Medical SchoolChiba UniversityHyogo College of MedicineNational Defense Medical CollegeFukuoka UniversityFondation pour la Recherche MédicaleUniversity of Occupational and Environmental HealthKeio UniversityTokyo Medical and Dental UniversityHokkaido UniversityNational Center for Global Health and MedicineNihon UniversityTeikyo University School of MedicineSchool of Medicine, Kyorin University
KeywordsMedicineVital signsHazard ratioSepsisCohort studyInternal medicineCohortBlood pressureRetrospective cohort studyMortality rateHypothermiaEmergency medicineConfidence intervalSurgery

Abstract

fetched live from OpenAlex

BACKGROUND: Elderly patients have a blunted host response, which may influence vital signs and clinical outcomes of sepsis. This study was aimed to investigate whether the associations between the vital signs and mortality are different in elderly and non-elderly patients with sepsis. METHODS: This was a retrospective observational study. A Japanese multicenter sepsis cohort (FORECAST, n = 1148) was used for the discovery analyses. Significant discovery results were tested for replication using two validation cohorts of sepsis (JAAMSR, Japan, n = 624; SPH, Canada, n = 1004). Patients were categorized into elderly and non-elderly groups (age ≥ 75 or < 75 years). We tested for association between vital signs (body temperature [BT], heart rate, mean arterial pressure, systolic blood pressure, and respiratory rate) and 90-day in-hospital mortality (primary outcome). RESULTS: In the discovery cohort, non-elderly patients with BT < 36.0 °C had significantly increased 90-day mortality (P = 0.025, adjusted hazard ratio 1.70, 95% CI 1.07-2.71). In the validation cohorts, non-elderly patients with BT < 36.0 °C had significantly increased mortality (JAAMSR, P = 0.0024, adjusted hazard ratio 2.05, 95% CI 1.29-3.26; SPH, P = 0.029, adjusted hazard ratio 1.36, 95% CI 1.03-1.80). These differences were not observed in elderly patients in the three cohorts. Associations between the other four vital signs and mortality were not different in elderly and non-elderly patients. The interaction of age and hypothermia/fever was significant (P < 0.05). CONCLUSIONS: In septic patients, we found mortality in non-elderly sepsis patients was increased with hypothermia and decreased with fever. However, mortality in elderly patients was not associated with BT. These results illuminate the difference in the inflammatory response of the elderly compared to non-elderly sepsis patients.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.264

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.038
GPT teacher head0.322
Teacher spread0.284 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations97
Published2020
Admission routes2
Has abstractyes

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