Quality of Life During the First 3 Months Following Discharge after Surgery for Head and Neck Cancer: Prospective Evaluation
Bibliographic record
Abstract
OBJECTIVE: To identify patient groups that are prone to poorer quality of life (QoL) during the first 3 months following discharge from the hospital after surgery for head and neck cancer. DESIGN: Prospective evaluation of the QoL of surgically treated head and neck cancer patients measured with questionnaires at discharge and at 6 weeks and 3 months after discharge. SETTING: Department of Otolaryngology and Head and Neck Surgery of the Erasmus University Medical Centre, a tertiary health care centre in Rotterdam, The Netherlands. PARTICIPANTS: Ninety head and neck cancer patients who had undergone a total laryngectomy, neck dissection, or the commando procedure. MAIN OUTCOME MEASURES: Patients' quality of life in 22 different dimensions. RESULTS: Three patient characteristics associated with poorer QoL during the first 3 months following discharge from the hospital after surgery for head and neck cancer: laryngectomy, lower levels of education, and being single. QoL already improved in eight QoL dimensions during the first 3 months after discharge, but QoL in the dimensions "loss of control" and "physical self-efficacy" worsened during this same period. CONCLUSIONS: It is possible to identify patient groups that are prone to poorer QoL during the first 3 months following discharge from the hospital after surgery for head and neck cancer. The results of this study may help care providers working with head and neck cancer patients to tailor their rehabilitation programs.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".