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Record W3017219754 · doi:10.5435/jaaos-d-20-00391

Recommendations for the Care of Pediatric Orthopaedic Patients During the COVID-19 Pandemic

2020· review· en· W3017219754 on OpenAlexaff
Sarah Farrell, Emily K. Schaeffer, Kishore Mulpuri

Bibliographic record

VenueJournal of the American Academy of Orthopaedic Surgeons · 2020
Typereview
Languageen
FieldMedicine
TopicCOVID-19 and healthcare impacts
Canadian institutionsBC Children's Hospital
Fundersnot available
KeywordsMedicinePandemicCoronavirus disease 2019 (COVID-19)Intensive care medicineLimitingHealth careMEDLINESevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Medical emergencyDiseasePathology

Abstract

fetched live from OpenAlex

The COVID-19 pandemic has necessitated modifications to pediatric orthopaedic practice to protect patients, families, and healthcare workers and to minimize viral transmission. It is critical to balance the benefits of alterations to current practice to reduce the chances of COVID-19 infection, with the potential long-term impact on patients. Early experiences of the pandemic from orthopaedic surgeons in China, Singapore, and Italy have provided the opportunity to take proactive and preventive measures to protect all involved in pediatric orthopaedic care. These guidelines, based on expert opinion and best available evidence, provide a framework for the management of pediatric orthopaedic patients during the COVID-19 pandemic. General principles include limiting procedures to urgent cases such as traumatic injuries and deferring outpatient visits during the acute phase of the pandemic. Nonsurgical methods should be considered where possible. For patients with developmental or chronic orthopaedic conditions, it may be possible to delay treatment for 2 to 4 months without substantial detrimental long-term impact.

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 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)0.003
metaresearch head score (Gemma)0.010
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 score0.012
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0030.001
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0120.004

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 head0.133
GPT teacher head0.431
Teacher spread0.298 · 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

Citations66
Published2020
Admission routes1
Has abstractyes

Explore more

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