Morbidity and Cost Differences between Free Flap Reconstruction and Pedicled Flap Reconstruction in Oral and Oropharyngeal Cancer: Matched Control Study
Bibliographic record
Abstract
BACKGROUND: To compare morbidity and cost in patients who underwent primary reconstruction with free tissue transfer with those with pectoralis major myocutaneous flap (PMMF) reconstructions after ablation of oral and oropharyngeal squamous cell carcinoma. METHODS: Over a 6-year period, 36 patients had PMMF reconstructions and 127 patients had a variety of free flap (FRF) reconstructions after oral and oropharyngeal cancer ablation. Correction for confounding patient and disease variables was performed by matching all PMMF patients to a randomly selected cohort of FRF patients for age, sex, International Union Against Cancer-American Joint Committee on Cancer T category, tumour subsite, and radiotherapy status. This resulted in two groups of 32 patients each, with the flap reconstruction being the only variable. The following outcome variables were analyzed: operative time; blood loss; admission length, including intensive care unit and coronary care unit stay; complications; secondary interventions; readmissions; and feeding status. Cumulative costs of nursing care, hospital supplies and charges, surgeon's fees, and anesthesiologist's fees were calculated. Statistical analysis applied the paired Student's t-test and the chi-square test. RESULTS: Of all morbidity parameters, only operative time was significantly longer for FRF (p < .0001). Analysis of other outcome variables and costs showed no statistically significant differences (p > .05). CONCLUSION: There is no evidence that morbidity and cost differ between the pedicled flap and free tissue transfer reconstruction strategies, other than lengthier operating room time for FRF.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".