Considerations in Head and Neck Oncologic Reconstructions and Microsurgery During COVID-19 Pandemic
Bibliographic record
Abstract
COVID outbreak has incapacitated the healthcare system around the world. Existing resources and manpower are being redirected to take care of the COVID-19 disease patients. People with head and neck cancers with the need of post ablative reconstruction are in difficult situation owing to multiple factors like poor general condition, disease progression and potential chance of getting an infection of COVID -19 in a health care facility as well as doubt regarding treatment completion i.e. adjuvant treatment. Appropriate reconstruction following ablative surgery, especially in advanced disease, facilitates functional recovery and thus adding to the quality of life of the patients.The reconstructive procedures are resource-intensive, requiring long hours of surgery, trained manpower, and multiple team members. However, if adequate surgical excision demands the reconstructive procedure, then it should not be a hindrance for the standard treatment. We need to review our approach in the face of the devastating COVID-19 pandemic. We are presently working in resource constraints like limited availability of staff and limited availability of personal protective equipment especially in plastic surgery procedures which requires the use of loupes and microscope. Thus, the challenge is to ensure proper reconstruction with limited available resources and maintaining safety standards for the staff in the operation theatre. This work is based on our experience and evidence from the literature.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".