Estimating the potential overdiagnosis and overtreatment of acute appendicitis in Thailand using a secondary data analysis of service utilization before, during and after the COVID-19 lockdown policy
Bibliographic record
Abstract
Acute appendicitis is one of the most common surgical emergencies; however, optimal diagnosis and treatment of acute appendicitis remains challenging. We used the coronavirus disease 2019 (COVID-19) lockdown policy as a natural experiment to explore potential overdiagnosis and overtreatment of acute appendicitis in Thailand. The aim of this study was to estimate the potential overdiagnosis and overtreatment of acute appendicitis in Thailand by examining service utilization before, during, and after the COVID-19 lockdown policy.A secondary data analysis of patients admitted with acute appendicitis under the Universal Coverage Scheme (UCS) in Thailand over a 6-year period between 2016 and 2021 was conducted. The trend of acute appendicitis was plotted using a 14-day rolling average of daily cases. Patient characteristics, clinical management, and outcomes were descriptively presented and compared among three study periods, namely pre-pandemic, lockdown, and post-lockdown.The number of overall acute appendicitis cases decreased from 25,407 during pre-pandemic to 22,006 during lockdown (13.4% reduction) and 21,245 during post-lockdown (16.4% reduction). This reduction was mostly due to a lower incidence of uncomplicated acute appendicitis, whereas cases of generalized peritonitis were scarcely affected by the pandemic. There was an increasing trend towards the usage of diagnostic computerized tomography for acute appendicitis but no significant difference in treatment modalities and complication rates.The stable rates of generalized peritonitis and complications during the COVID-19 lockdown, despite fewer admissions overall, suggest that there may have been overdiagnosis and overtreatment of acute appendicitis in Thailand. Policy makers could use these findings to improve clinical practice for acute appendicitis in Thailand and support the efficient utilization of surgical services in the future, especially during pandemics.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".