Submental island flap reconstruction reduces cost in oral cancer reconstruction compared to radial forearm free flap reconstruction: A case series and cost analysis
Bibliographic record
Abstract
BACKGROUND: In Canada, 4,400 cases of oral cancer are diagnosed yearly. Surgical resection is a key component of treatment in many of these cancers. Reconstruction of defects, with the goal of preserving function, is of utmost importance. Several choices are possible for reconstruction of larger defects, including both free and pedicled flaps. Free flap reconstruction is reliable and effective, but requires additional personnel and peri-operative resources. Pedicled flaps remain an important alternative to free flaps, and are less resource intensive. This paper reviews our inaugural experience with the submental island flap (SIF) and compares costs incurred to a matched cohort of oral cancer patients reconstructed with forearm free flaps. METHODS: Charts of patients who underwent SIF and RFFF reconstruction from January 1st 2013 to April 1st 2015 were retrospectively examined. Associated costs were obtained via online database and previously reported costs at the study institution. RESULTS: Mean length of ICU stay in glossectomy RFFF reconstruction was 4.7 days. Only one patient required ICU stay for one night in the SIF group. Mean length of hospital stay was not significantly different in SIF patients vs RFFF patients (12.4 vs 15.4 days, p > 0.05). Mean operative time was significantly lower in the SIF group compared to the RFFF group (347 vs 552 min, p < 0.05). Total mean intraoperative costs were found to be $4780.59 for RFFF operations, versus $2307.94 for SIF. Total mean cost of post-operative stay was $18158.40 in the SIF group and $43617.60 in the RFFF group. Total cost savings were therefore $27931.85 per patient for the SIF group. CONCLUSIONS: We have demonstrated the use of the submental island flap as an alternative to radial forearm free flaps, showing both decreased hospital costs and comparable patient outcomes. Pedicled flaps are making a resurgence in head and neck reconstruction, and the submental island flap offers an excellent alternative to more labour intensive and costly free flap alternatives.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".