Evaluation of a Nurse-led Aftercare Intervention for Patients with Head and Neck Cancer Treated with Radiotherapy and Cisplatin or Cetuximab
Bibliographic record
Abstract
The supportive needs for head and neck cancer (HNC) patients during the vulnerable period after treatment are not always met. Therefore, more professional support regarding physical, social, and psychological care as well as lifestyle is recommended.This study is an evaluation of a nurse-led aftercare intervention to support patients recovering from HNC treatment.Intervention group (IG) participants received 2 extra consultations from a nurse practitioner 3 and 9 months after treatment of HNC. A holistic conversational tool, the Self-Management Web, was developed to guide the nurse through the conversation. Primary outcomes were health-related quality of life (HRQoL) and quality of patient-centered care. A secondary outcome was self-management skills.Twenty-seven patients were included in the IG, and 28 were included in the control group. Differences in HRQoL and self-management between the IG and the control group were not statistically significant. For the IG, all domains of the Self-Management Web were perceived important and addressed by the nurse practitioner.This holistic nurse-led aftercare intervention was highly appreciated by HNC patients. Although the intervention met the need for support in recovery after treatment, it did not improve HRQoL or self-management skills.For both nurses and patients, the intervention is feasible and acceptable in daily practice. Self-management support for patients after their cancer treatment is of added value and has potential to improve the quality of regular follow-up care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".