Maintaining cancer services during the COVID-19 pandemic: the Aotearoa New Zealand experience
Bibliographic record
Abstract
COVID-19 caused significant disruption to cancer services around the world. The health system in Aotearoa New Zealand has fared better than many other regions, with the country being successful, so far, in avoiding sustained community transmission. However, there was a significant initial disruption to services across the cancer continuum, resulting in a decrease in the number of new diagnoses of cancer in March and April 2020. Te Aho o Te Kahu, Aotearoa New Zealand's national Cancer Control Agency, coordinated a nationwide response to minimise the impact of COVID-19 on people with cancer. The response, outlined in this paper, included rapid clinical governance, a strong equity focus, development of national clinical guidance, utilising new ways of delivering care, identifying and addressing systems issues and close monitoring and reporting of the impact on cancer services. Diagnostic procedures and new cancer registrations increased in the months following the national lockdown, and the cumulative number of cancer registrations in 2020 surpassed the number of registrations in 2019 by the end of September. Cancer treatment services - surgery, medical oncology, radiation oncology and haematology - continued during the national COVID-19 lockdown in March and April 2020 and continued to be delivered at pre-COVID-19 volumes in the months since. We are cautiously optimistic that, in general, the COVID-19 pandemic does not appear to have increased inequities in cancer diagnosis and treatment for Māori in Aotearoa New Zealand.
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.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".