Determining the unmet survivorship needs in patients with head and neck cancer: A prospective, longitudinal study.
Bibliographic record
Abstract
e24088 Background: Head and neck cancer patients commonly have unmet needs. Our purpose was to determine the number, type, and predictors of unmet survivorship needs in patients with head and neck cancer over their cancer journey. Methods: Patients with primary, non-metastatic head and neck cancer were prospectively enrolled prior to curative intent treatment (surgery, radiation and or systemic therapy in any combination). Patients completed baseline demographics, the Cancer Survivors' Unmet Needs Measure (CaSUN) and the FACT-HN and then again 3, and 6 months post-treatment. Mean unmet needs at each time point were calculated. Univariable and stepwise multivariable analyses of predictors for number of unmet needs were performed at each time point. Results: Among 187 participants, median age was 64 (31-88) and 148 (79%) were male. Education was beyond high school in 117 (63%) and 63 (41%) were working at diagnosis. Most patients had oropharynx (n = 84; 46%), hypopharynx/larynx (n = 40; 22%) or oral cavity (n = 28;15%) cancers. At least one unmet need was reported by 68%, 52%, and 56% at baseline, 3 and 6 months. The mean number of unmet needs was 7.5 ± 9.1 at baseline, 3.8 ± 6 at 3 months and 5.4 ± 8.5 at 6 months. Higher education level [3.24 (0.24,6.24); p = 0.035] and worse QOL [-0.14 (-0.22,-0.06); p < 0.01] were associated with increased survivorship unmet needs on multivariable analysis at baseline. Worse QOL was associated with increased survivorship unmet needs on multivariable analysis at 3 [-0.21 (-0.31,-0.12); p < 0.01] and 6 months [-0.27 (-0.49,-0.06); p < 0.01]. Table describes the top unmet needs. Conclusions: A high proportion of patients with head and neck cancer have unmet needs, more commonly in patients with poorer quality of life. Post-treatment, unmet needs were more often related to treatment toxicities and their impact on quality of life. [Table: see text]
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".