Impact of the COVID-19 pandemic on the operations of the specialty hospital department in Beijing
Bibliographic record
Abstract
The coronavirus disease 2019 (COVID-19) pandemic was disruptive to non-COVID-19-related healthcare. This study aimed to compare patient inflows and patient population characteristics at the Hand Surgery Department of JiShuiTan (JST) Hospital in Beijing, a top referral center, in 2020 during the pandemic relative to the same period in 2019. This cross-sectional study was conducted to analyze the impact of the COVID-19 pandemic on patients admitted to the hand surgery ward. The participants were patients admitted from January to April 2019 (J-A19) and from January to April 2020 (J-A20). The medical records were analyzed, including age, sex, admission time, admission mode, admission diagnosis, and patient residence. Significantly fewer patients were admitted in J-A20 than in J-A19, with particularly dramatic reductions observed for non-Beijing residents and nonemergency cases (e.g., congenital anomalies operations for children). The top 5 diagnosis types for admitted patients were consistent throughout J-A19 and in January 2020. The rank of the diagnostic type "open injuries of the hand and wrist" increased significantly in February, March, and April of 2020 compared with 2019. The COVID-19 pandemic decreased patient admissions, especially for nonemergency patients, during J-A20.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".