Synthesis of Recommendations From 25 Countries and 31 Oncology Societies: How to Navigate Through Covid-19 Labyrinth
Bibliographic record
Abstract
INTRODUCTION: Pandemic COVID-19 is an unexpected challenge for the oncological community, indicating potential detrimental effects on cancer patients. Our aim was to summarize the converging key points providing a general guidance in order to support decision making, pertaining to the oncologic care in the middle of a global outbreak. METHODS: We did an international online search in twenty five countries that have managed a surge in cancer patient numbers. We collected the recommendations from thirty one medical oncology societies. RESULTS: By synthesizing guidelines for a) oncology service delivery adjustments, b) general and specific treatment adaptations, and c) discrepancies from guidelines comparison, we present a clinical synopsis with the forty more crucial statements. A Covid-19 risk stratification base was also created in order to obtain a quick, objective patient assessment and a risk-benefit evaluation on a case-by-case basis. CONCLUSIONS: In an attempt to face these complex needs and due to limited understanding of COVID-19, a variability of recommendations based on general epidemiological and infectious disease principles rather than definite cancer-related evidence has evolved. Additionally, the absence of an effective treatment or vaccine requires the development of cancer management guidance, capitalizing on comprehensive COVID-19 oncology experience globally.
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 | 0.038 | 0.120 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.015 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".