NECOM Skincare Algorithm for Patients With Cancer and Survivors
Bibliographic record
Abstract
Background: Cancer treatment-related cutaneous adverse events (cAEs) frequently occur, which can interfere with anticancer treatment outcomes and can severely impact quality of life for patients. Methods: The Nordic European Cutaneous Oncodermatology Management (NECOM) project aims to improve cancer patient outcomes by offering tools for preventing and managing cAEs. The first NECOM paper explored clinical insights in cAEs and focused on skincare regimens involving hygiene, moisturization, sun protection, and camouflage products. A skincare algorithm for patients with cancer and survivors follows this article to promote healthy skin and reduce cancer treatmentrelated cAEs. Results: The NECOM panel discussed and reached a consensus on an evidence- and opinion-based practical algorithm for oncology skin care to support all stakeholders in the Nordic European health care setting. The oncology nurse is central in coordinating individual patient’s cancer care and performing triage for cAEs, seeking urgent care via an oncologist and/or the emergency department if needed. The care organization of the presented cAEs depends on the patient’s general health and skin condition and the health care system. Conclusion: Communication on state-of-the-art treatment in the fast-evolving area of oncology is necessary to provide tailored general measures and skin care for cAEs supported by evidence and practice-based expert recommendations
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".