Patient-reported outcomes and morbidity after head and neck reconstructions: An evaluation of fibular and scapular free flaps
Bibliographic record
Abstract
BACKGROUND: Fibular (FFF) and scapular free flaps (SFF) are versatile tissue transfers for head and neck reconstruction. However, their relative morbidity has been sparsely studied. The primary goal of this study was to evaluate the morbidity and patient-reported outcome measures of these two reconstructive options. MATERIALS AND METHODS: Case series of patients from 2017 to 2020 who underwent a FFF or SFF for head and neck ablation. Demographic and surgical outcome measures, such as Charlson Comorbidity Index (CCI), anesthetic time, donor site morbidity, and perioperative morbidity score (POMs) were extracted. Patients were contacted to complete the Decision Regret Scale (DRS), University of Washington Quality of Life (UW-QoL), Oral Health Impact-14, and limb specific functional outcome measures. Statistical analyses included a linear regression. RESULTS: In total, 97 FFF (mean age 58.5, 62.9% male) and 55 SFF (mean age 64.8, 63.6% male) were included. Total surgical time was higher in the SFF group (p < 0.05) and they had more comorbidities (p < 0.01). SFF patients had lower POM scores on post-operative day three (p < 0.05) while FFF patients scored better on the UW-QoL Physical Domain (p < 0.01). The DRS for both groups (FFF mean DRS 22.7, SFF mean DRS 19.2) was similar. When adjusted for patient morbidity, however, the SFF group had less decisional regret (p < 0.05). CONCLUSION: This is the largest comprehensive evaluation of patient-reported outcome measures for FFF and SFFs. SFFs required longer surgical times but had less early morbidity than FFFs. Patients who underwent either reconstructions reported mild decisional regret, proving these are generally well tolerated procedures.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".