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Record W3036895700 · doi:10.1177/1759720x20934276

Impact of COVID-19 on orthopaedic care: a call for nonoperative management

2020· review· en· W3036895700 on OpenAlexaff
Mark Phillips, Yaping Chang, Robert Zura, Samir Mehta, Peter V. Giannoudis, Peter A. Nolte, Mohit Bhandari

Bibliographic record

VenueTherapeutic Advances in Musculoskeletal Disease · 2020
Typereview
Languageen
FieldMedicine
TopicCOVID-19 and healthcare impacts
Canadian institutionsMcMaster UniversityImpact
Fundersnot available
KeywordsPostponementMedicineElective surgeryCoronavirus disease 2019 (COVID-19)PandemicIntensive care medicineGeneral surgerySurgeryOperations managementDiseaseInfectious disease (medical specialty)Internal medicine

Abstract

BACKGROUND: Surgical specialties face unique challenges caused by SARS-COV-2 (COVID-19). These disruptions will call on clinicians to have greater consideration for non-operative treatment options to help manage patient symptoms and provide therapeutic care in lieu of the traditional surgical management course of action. This study aimed to summarize the current guidance on elective surgery during the COVID-19 pandemic, assess how this guidance may impact orthopaedic care, and review any recommendations for non-operative management in light of elective surgery disruptions. METHODS: A systematic search was conducted, and included guidance were categorized as either "Selective Postponement" or "Complete Postponement" of elective surgery. Selective postponement was considered as guidance that suggested elective cases should be evaluated on a case-by-case basis, whereas complete postponement suggested that all elective procedures be postponed until after the pandemic, with no case-by-case consideration. In addition, any statements regarding conservative/non-operative management were summarized when provided by included reports. RESULTS: A total of 11 reports from nine different health organizations were included in this review. There were seven (63.6%) guidance reports that suggested a complete postponement of non-elective surgical procedures, whereas four (36.4%) reports suggested the use of selective postponement of these procedures. The guidance trends shifted from selective to complete elective surgery postponement occurred throughout the month of March. The general guidance provided by these reports was to have an increased consideration for non-operative treatment options whenever possible and safe. As elective surgery begins to re-open, non-operative management will play a key role in managing the surgical backlog caused by the elective surgery shutdown. CONCLUSION: Global guidance from major medical associations are in agreement that elective surgical procedures require postponement in order to minimize the risk of COVID-19 spread, as well as increase available hospital resources for managing the influx of COVID-19 patients. It is imperative that clinicians and patients consider non-operative, conservative treatment options in order to manage conditions and symptoms until surgical management options become available again, and to manage the increased surgical waitlists caused by the elective surgery shutdowns.

Stored with the screening record, where it is evidence for the labels above.

How this classification was reachedexpand

The three-model screen

all 5,600 screened works →

All three models called this out of scope.

stratum: aff_core · design weight: 5595.24 (the sample is stratified; any rate computed without the weight is wrong)
Claude Opus 4.8OUT
genre: empirical
about Canada: no
confidence: medium

Systematic search summarizing COVID surgical guidance to inform care; uses a review to answer a clinical-management question.

GPT-5.6 (high)OUT
genre: empirical
about Canada: no
confidence: high

This synthesizes clinical guidance on orthopaedic care during COVID-19, not evidence-synthesis methodology.

Grok 4.5OUT
genre: conceptual
about Canada: no
confidence: high

Review of elective orthopaedic surgery guidance during COVID; clinical care pathways, not research practice.

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.016
metaresearch head score (Gemma)0.081
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.016
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.081
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0060.007
Science and technology studies0.0010.001
Scholarly communication0.0030.004
Open science0.0020.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0020.001

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 head0.068
GPT teacher head0.493
Teacher spread0.426 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations35
Published2020
Admission routes1
Has abstractyes

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