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Record W3132671067 · doi:10.1503/cjs.022520

COVID-19: pivoting from in-person to virtual orthopedic surgical evaluation

2021· article· en· W3132671067 on OpenAlexafffundvenue
Andrew Roberts, Geoffrey Johnston, COLIN D. LANDELLS

Bibliographic record

VenueCanadian Journal of Surgery · 2021
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 and healthcare impacts
Canadian institutionsUniversity of British Columbia
FundersFaculty of Medicine, University of British Columbia
KeywordsMedicineVideoconferencingCoronavirus disease 2019 (COVID-19)TelemedicineOrthopedic surgeryPandemicThe InternetMedical emergencySevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)SurgeryHealth careMultimediaDiseaseWorld Wide Web

Abstract

fetched live from OpenAlex

<h3>Summary</h3> In March 2020, the coronavirus disease 2019 (COVID-19) pandemic necessitated substantial downscaling of office-based orthopedic surgical practice. To address the ongoing need for patient assessment, surgical practices pivoted from in-person appointments to a virtual platform. Patients (<i>n</i> = 1823), contacted by telephone (82%) or by video (18%), judged this new approach as excellent or very good in 71% of telephone contacts, and in 84% of those successfully inter-viewed by video. For future meetings, 4 of 5 patients preferred virtual rather than in-person contact. Patients whose round-trip travel time for in-person appointments was under 2 hours were twice as likely to prefer future in-person contact as those more than 2 hours away. Patients who had far to travel or who used walking aids were more likely to travel accompanied. Acknowledging that patients value both videoconferencing and telephone contact, surgeons should offer virtual visits as an alternative to in-person assessments. Patients need to have access to reliable Internet. Finally, telemedicine is environmentally friendly.

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)0.003
metaresearch head score (Gemma)0.017
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.247
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.210
GPT teacher head0.405
Teacher spread0.195 · 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

Citations9
Published2021
Admission routes3
Has abstractyes

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