Survey of the Impact of COVID-19 on Pediatric Orthopaedic Surgeons Globally
Bibliographic record
Abstract
BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic required rapid, global health care shifts to prioritize urgent or pandemic-related care and minimize transmission. Little is known about impacts on pediatric orthopaedic surgeons during this time. We aimed to investigate COVID-19-related changes in practice, training, and research among pediatric orthopaedic surgeons globally. METHODS: An online survey was administered to orthopaedic surgeons with interest in pediatrics in April 2020 and a follow-up was administered in February 2021. The surveys captured demographics and surgeons' self-reported experiences during the pandemic. Participants were recruited from web media and available email lists of orthopaedic societies over a 2-month period. Descriptive statistics were used to analyze results, stratified by the severity of local COVID-19-related measures. RESULTS: A total of 460 responses from 45 countries were collected for initial survey. Of these, 358 (78.5%) respondents reported lockdown measures in their region at time of survey. Most (n=337, 94.4%) reported pausing all elective procedures. Surgeons reported a reduction in the average number of surgeries per week, from 6.89 (SD=4.61) prepandemic to 1.25 (SD=2.26) at time of survey (mean difference=5.64; 95% confidence interval=5.19, 6.10). Average number of elective outpatient appointments per week decreased from 67.89 (SD=45.78) prepandemic to 11.79 (SD=15.83) at time of survey (mean difference=56.10, 95% confidence interval=5.61, 60.58). In total, 177 (39.4%) surgeons reported using virtual modes of outpatient appointments for the first time. Of 290 surgeons with trainees, 223 (84.5%) reported implementation of systems to continue training such as webinars or virtual rounds. Of 192 respondents with research, 149 (82.8%) reported continuing research activities during the pandemic with most reporting either cessation (n=75, 64.15%), or reduction (n=25, 29.9%) in participant recruitment. A total of 111 responses from 28 countries were collected during follow-up. Surgeons described policy and circumstantial changes that facilitated resumption of clinical work. CONCLUSIONS: The COVID-19 pandemic and its related counter measures have had significant impacts on pediatric orthopaedic practice and increased uptake of technology to provide care continuity. Rigorous epidemiological studies are needed to assess impacts of delayed and virtual care on patient outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".