Consensus Statement: Summary of the Quebec Lung Cancer Network Recommendations for Prioritizing Patients with Thoracic Cancers in the Context of the COVID-19 Pandemic
Bibliographic record
Abstract
Background: The emergence of covid-19 has the potential to change the way in which the health care system can accommodate various patient populations and might affect patients with non-covid-19 problems. The Quebec Lung Cancer Network, which oversees thoracic oncology services in the province of Quebec under the direction of the Ministère de la Santé et des Services sociaux, convened to develop recommendations to deal with the potential disruption of services in thoracic oncology in the province of Quebec. The summary provided here has been adapted from the original document posted on the Programme québécois du cancer Web site at: https://www.msss.gouv.qc.ca/professionnels/documents/coronavirus-2019-ncov/PJ1_Recommandations_oncologie-thoracique-200415.pdf. Methods: Plans to optimize the health care system and potentially to prioritize services were discussed with respect to various levels of activity. For each level-of-activity scenario, suggestions were made for the services and treatments to prioritize and for those that might have to be postponed, as well as for potential alternatives to care. Results: The principal recommendation is that the cancer centre executive committee and the multidisciplinary tumour board always try to find a solution to maintain standard-of-care therapy for all patients with thoracic tumours, using novel approaches to treatment and the adoption of a network approach to care, as needed. Conclusions: The effect of the covid-19 pandemic on the health care system remains unpredictable and requires that cancer teams unite and offer the most efficient and innovative therapies to all patients under the various conditions that might be forced upon them.
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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 | 0.086 | 0.147 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.010 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.023 | 0.008 |
| Research integrity | 0.030 | 0.028 |
| Insufficient payload (model declined to judge) | 0.017 | 0.009 |
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".