The effect of healthcare disruptions during the <scp>COVID</scp> ‐19 pandemic on colposcopy services and practice: A systematic review and meta‐analysis
Bibliographic record
Abstract
Abstract Introduction The healthcare reorganization during the COVID‐19 pandemic affected colposcopy services and cervical cancer prevention, particularly in those countries where healthcare systems were already under‐resourced. This review aimed to quantify the reduction in colposcopy services across countries during the COVID‐19 pandemic and to determine whether the data source per study and cervical cancer screening coverage per country influenced the extent of these reductions. Material and Methods Studies reporting comparative data on colposcopy services between the COVID‐19 pre‐pandemic and pandemic period were included. MEDLINE, Embase, EMCare, Covid‐19 Research, British Nursing Index, APA PsycINFO, and Allied and Complimentary Medicine databases were searched for studies published from March 2020 to December 2023. The Newcastle−Ottawa scale was used for risk of bias assessment. The number of colposcopies, cervical treatments, pre‐invasive lesions diagnoses, and cervical cancer diagnoses per month were compared between the pre‐pandemic (before March 2020) and pandemic period (after March 2020). The effect measure was the standardized mean difference. Heterogeneity was evaluated with the chi‐squared test and quantified with the I 2 method. A meta‐regression was performed, considering the data source (regional/national databases/registries or institutional databases) and the screening coverage according to World Health Organization data (≥70% or <70%) as moderators. The review was registered on PROSPERO (CRD42023447188). Results Thirteen studies were included. Twelve were of good/high quality according to the Newcastle−Ottawa scale. The standardized mean difference between the pre‐pandemic and pandemic periods was −1.60 (95% CI −2.49 to −0.72, p = 0.004) for colposcopies (4 studies, I 2 = 60.97%, p = 0.075), −1.70 (95% CI −2.50 to −0.90, p < 0.001) for cervical treatments (5 studies, I 2 = 52.92%, p = 0.081), −4.61 (95% CI ‐7.90 to −1.33, p = 0.006) for pre‐invasive lesion diagnoses (4 studies, I 2 = 92.45%, p < 0.001), and −0.85 (95% CI −1.52 to −0.19, p = 0.012) for cervical cancer diagnoses (9 studies, I 2 = 71.07%, p = 0.002). At meta‐regression, further reductions for cervical treatments and pre‐invasive lesion diagnoses were observed in the case of screening coverage <70%. Conclusions During the COVID‐19 pandemic, a reduction in colposcopies, cervical treatments, pre‐invasive lesions diagnoses, and invasive cancer diagnoses was observed. Since a screening coverage of <70% heightened these declines, increasing such coverage could lead to better resilience of cervical cancer prevention services to future crises.
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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 | 0.014 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.035 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".