Development of a QOL module for head and neck cancer patients with enteral feeding tubes
Bibliographic record
Abstract
6153 Background: Head and neck cancer (HNC) patients frequently require enteral feeding (EF) during treatment, but how this affects quality of life (QOL) is unknown. We developed a QOL module for use with existing HNC disease-specific QOL questionnaires in patients with EF tubes. Methods: Literature review and a clinician focus group were used to generate QOL items. Items were formatted as statements rated on a 5 point Likert-type scale. The prototype was pilot tested in 12 HNC patients with current or recent EF tubes. The resulting beta version underwent item reduction in a non-overlapping sample of 36 HNC patients, who indicated the importance of each item. Items were kept if their summary importance scores were ranked above median by the whole group or by Chinese or female minority subgroups. Results: Items from the literature were provided to a focus group representing radiation oncology, interventional radiology, radiation therapy, nursing, and nutrition. Of 41 items generated, after pilot testing 15 were eliminated; 2 items identified by 2 or more patients, and 2 “positive” items suggested by investigators, were added. The item reduction study sample of 36 patients (mean age 57.5) included 20 Caucasian individuals, 9 Chinese, and 7 of other cultural background, with 12 females and 24 males. Cancer sites included nasopharynx (16), oropharynx (9), oral cavity (5), hypopharynx (2), unknown primary (2), larynx (1), and paranasal sinus (1). Overall, 17 items rated above median importance were kept. Among Chinese patients, 2 additional items were kept, and among women 1 additional item was kept. No additional write-in items were identified by 2 or more patients. Conclusions: We have developed a 20 item QOL module for HNC patients with EF tubes. The module is currently undergoing validity and reliability testing in a new sample of 75 patients with EF tubes. This instrument may be useful in trials comparing aggressive treatment strategies for HNC or different methods of EF. No significant financial relationships to disclose.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".