COVID-19 Pandemic and its Impact on the Management of Head and Neck Cancer in the Spanish Healthcare System.
Bibliographic record
Abstract
Abstract: Introduction Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has represented a major challenge for healthcare systems worldwide, changing the habits of physicians. A reorganization of healthcare activity has been necessary, limiting surgical activity to essential cases (emergencies and oncology), and improving the distribution of health resources. Objective To analyze the impact of the COVID-19 pandemic on head and neck cancer surgery management in Spain. Methods A cross-sectional study, through an anonymous and voluntary online survey distributed to 76 Spanish otorhinolaryngology departments. Results A total of 44 centers completed the survey, 65.9% of which were high-volume. A total of 45.5% of them had to stop high-priority surgery and 54.5% of head and neck surgeons were relocated outside their scope of practice. Surgeons reported not feeling safe during their usual practice, with a decrease to a 25% of airway procedures. A total of 29.5% were “forced” to deviate from the “standard of care” due to the epidemiological situation. Conclusions Approximately half of the departments decreased their activity, not treating their patients on a regular basis, and surgeons were reassigned to other tasks. It seems necessary that the head and neck surgeons balance infection risk with patient care. The consequences of the reported delays and changes in daily practice should be evaluated in the future in order to understand the real impact of the pandemic on the survival of head and neck cancer patients.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".