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Does COVID-19 infection change the need for future surgical interventions? An exploratory analysis.

2021· preprint· en· W3214426667 on OpenAlexafffundabout
Blayne Welk, Lucie Richard

Bibliographic record

VenueF1000Research · 2021
Typepreprint
Languageen
FieldMedicine
TopicCOVID-19 and healthcare impacts
Canadian institutionsWestern University
FundersSchulich School of Medicine and DentistryOntario Ministry of Health and Long-Term CareAcademic Medical Organization of Southwestern OntarioLawson Health Research Institute
KeywordsOpen peer reviewCoronavirus disease 2019 (COVID-19)Plant biologyPsychological interventionPhysiologyMedicineSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakIntensive care medicineOpen dataNeuroscienceBiologyVirologyPathologyComputer scienceOutbreakPsychiatryInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

<ns3:p> <ns3:bold>Background: </ns3:bold> It is unknown whether recovery from coronavirus disease 2019 (COVID-19) infection leads to an increased need for common surgical procedures. Our objective was to conduct an exploratory analysis of surgical procedures performed after a documented COVID-19 infection. </ns3:p> <ns3:p> <ns3:bold>Methods:</ns3:bold> We conducted a retrospective cohort study using routinely collected data from the province of Ontario, Canada. We identified individuals with a positive COVID-19 test between February 1 2020 and May 31 2020, and matched them 1:2 with individuals who had a negative COVID-19 test during the same period. We used physician billing codes to identify the ten most frequent surgical procedures in the COVID-19 cohort. An at-risk period 30 days after the first positive COVID-19 swab (or matched index date in the control group) until November 30 2020 was used. Cox proportional hazard models (adjusted for important baseline differences) are reported with hazard ratios (HR) and 95% confidence intervals. </ns3:p> <ns3:p> <ns3:bold>Results: </ns3:bold> After exclusions and matching, we had 19,143 people in the COVID-19 cohort, and 38,286 people in the control cohort. The top ten surgical procedures were hand/wrist fracture fixation, cesarean-section, ureteral stent placement, cholecystectomy, treatment of an upper tract urinary stone, hysterectomy, femur fracture repair, hip replacement, transurethral prostatectomy, and appendectomy. There was a significantly reduced hazard of requiring upper tract renal stone surgery (adjusted hazard ratio [aHR] 0.50, 95% confidence interval [CI] 0.29-0.87) or ureteral stent placement (aHR 0.54, 95%CI 0.36-0.82), or undergoing a cholecystectomy (aHR 0.43, 95%CI 0.26-0.71) among those with a prior positive COVID-19 test. </ns3:p> <ns3:p> <ns3:bold>Conclusions: </ns3:bold> After a COVID-19 infection there is not an increased risk of needing several common surgical procedures. There appears to be a reduced risk of renal stone disease treatment and ureteral stent placement, and a reduced risk of cholecystectomy, however understanding the reasons for this will require further study. </ns3:p>

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)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.526
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.396
GPT teacher head0.551
Teacher spread0.155 · 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.

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

Citations2
Published2021
Admission routes3
Has abstractyes

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