Impact of COVID-19 pandemic on the oncologic care continuum: urgent need to restore patients care to pre-COVID-19 era
Bibliographic record
Abstract
Abstract Background: Globally, cancer is the second leading cause of death, and it is estimated that over 18·1 million new cases are diagnosed annually. The COVID-19 pandemic has significantly impacted almost every aspect of the provision and management of cancer care worldwide. The time-critical nature of COVID-19 diagnosis and the large number of patients requiring hospitalisation necessitated the rerouting of already limited resources available for cancer services and programmes to the care of COVID-19 patients. Furthermore, the stringent social distancing, restricted in-hospital visits and lockdown measures instituted by various governments resulted in the disruption of the oncologic continuum including screening, diagnostic and prevention programmes, treatments and follow-up services as well as research and clinical trial programmes. Materials and Methods: We searched several databases from October 2020 to January 2021 for relevant studies published in English between 2020 and 2021 and reporting on the impact of COVID-19 on the cancer care continuum. This narrative review paper describes the impact of the COVID-19 pandemic on the cancer patient care continuum from screening and prevention to treatments and ongoing management of patients. Conclusions: The COVID-19 pandemic has profoundly impacted cancer care and the management of cancer services and patients. Nevertheless, the oncology healthcare communities worldwide have done phenomenal work with joint and collaborative efforts, utilising best available evidence-based guidelines to continue to give safe and effective treatments for cancer patients while maintaining the safety of patients, healthcare professionals and the general population. Nevertheless, several healthcare centres are now faced with significant challenges with the management of the backlog of screening, diagnosis and treatment cases. It is imperative that governments, leaders of healthcare centres and healthcare professionals take all necessary actions and policies focused on minimising further system-level delays to cancer screening, diagnosis, treatment initiation and clearing of all backlogs cases from the COVID-19 pandemic in order to mitigate the negative impact on cancer 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 | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".