Leveraging patient‐reported outcomes data to inform oncology clinical decision making: Introducing the FACE‐Q Head and Neck Cancer Module
Bibliographic record
Abstract
BACKGROUND: Existing patient-reported outcome measures (PROMs) used to assess patients with head and neck cancer have methodologic and content deficiencies. Herein, the development of a PROM that meets a range of clinical and research needs across head and neck oncology is described. METHODS: After development of the conceptual framework, which involved a literature review, semistructured patient interviews, and expert input, patients with head and neck cancer who were treated at Memorial Sloan Kettering Cancer Center were recruited by their surgeon. The FACE-Q Head and Neck Cancer Module was completed by patients in the clinic or was sent by mail. Rasch measurement theory analysis was used for item selection for final scale development and to examine reliability and validity. Scale scores for surgical defect and adjuvant therapy were compared with the cohort average to assess clinical applicability. RESULTS: The sample consisted of 219 patients who completed the draft scales. Fourteen independently functioning scales were analyzed. Item fit was good for all 102 items, and all items had ordered thresholds. Scale reliability was acceptable (person separation index was >0.75 for all scales; Cronbach α values were >.87 for all scales; test-retest ranged from 0.86 to 0.96). The scales performed well in a clinically predictable way, demonstrating functional and psychosocial differences across disease sites and with adjuvant therapy. CONCLUSIONS: The scales forming the FACE-Q Head and Neck Cancer Module were found to be clinically relevant and scientifically sound. This new PROM now is validated and ready for use in research and clinical care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".