Patient-Centered Outcomes in Non-Melanoma Skin Cancer Management: A Comprehensive Review
Bibliographic record
Abstract
Non-melanoma skin cancer (NMSC) is the most prevalent type of cancer worldwide, with a significantly rising incidence. While postoperative patient satisfaction and quality of life (QoL) are key metrics in cancer care, they are understudied with regard to NMSC care. This review aimed to summarize the existing data investigating the QoL outcomes after treatment of NMSC and the determinants of patient satisfaction in NMSC management. PubMed, Embase, Ovid MEDLINE, CINAHL, and Cochrane Library databases were searched up to December 1, 2023. Twenty eligible studies were identified, with 7 examining patient satisfaction, 12 examining QoL, and 1 looking at both. The studies used various tools, with the Patient Satisfaction Questionnaire being the most common for assessing patient satisfaction, and the Skin Cancer Index for QoL. Many factors (some controllable, others non-controllable) were found to influence postoperative patient satisfaction, such as preoperative QoL and interpersonal manners of the providers. QoL outcomes were often but not always linked with patient satisfaction and influenced by several factors, such as age, pretreatment mental health, and tumor localization. Clinicians should consider patient perspectives when determining the effectiveness of interventions for NMSC patients. Understanding the factors that influence patient satisfaction and QoL is crucial in delivering comprehensive patient care.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".