Impact of COVID-19 outbreak in knee arthroplasty in Chile: a cross-sectional, national registry-based analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Introduction: The need for beds and health personnel to treat coronavirus (COVID- 19) patients has led to the suspension of many elective sur-geries in Chile, including knee arthroplasties. This study aims to determine the incidence of knee arthroplasty in 2020, reflecting the effect of the COVID- 19 pandemic, and estimate the cost and time it would take to recover the waiting list prior to March 2020. Methods: A cross- sectional study was designed. We analyzed databases from The Department of Statistics and Health Information databases from Chile for 2019 and 2020, identifying patients with surgical discharges associated with knee arthroplasty codes. We estimated the time it would take to recover the surgeries unperformed in 2020 by simulating a monthly workload increase from the 2019 baseline. The costs of knee arthroplasty paid by the National Health Fund to institutions were estimated by diagnosis-related groups. Results: We found that the incidence rate of knee arthroplasty in 2020 decreased by 64% compared with 2019. The impact was higher in the public system (68%) and the National Health Found (63%). A simulated increase in knee arthroplasty productivity by 30% would allow recovering the postponed knee arthroplasty surgeries in 27 months, at a monthly cost to the public system of 318 million Chilean pesos (378 thousand US dollars). Conclusions: The incidence rate of knee arthroplasty during 2020 decreased by 64%, revealing the extensive waiting line for people with knee osteoarthritis. An increase between 20- 40% in productivity compared with 2019 would allow recovering the unperformed surgeries in 20 to 41 months, at a monthly cost to the public network between 210 and 425 million Chilean pesos (250 to 506 thousand US dollars).
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it