Surgical site infections following oral cavity cancer resection and reconstruction is a risk factor for plate exposure
Bibliographic record
Abstract
BACKGROUND: Plate-related complications following head and neck cancer ablation and reconstruction remains a challenging problem often requiring further management and reconstructive surgeries. We aim to identify an association between surgical site infections (SSI) and plate exposure. METHODS: A retrospective study between 1997 and 2014 was performed to study the association between postoperative SSI and plate exposures. Eligible patients included those with a history of oral squamous cell carcinoma who underwent surgical resection, neck dissection, and free tissue reconstruction. Demographic and treatment related information was collected. SSI were classified based on CDC definition and previously published literature. Univariable analysis on demographic factors, smoking history, diabetes, radiation, surgical and hardware related factors; while multivariable analysis on SSI, plate height, segmental mandibulectomy defects and radiation were conducted such as using cox proportional hazard models. RESULTS: Three hundred sixty-five patients were identified and included in our study. The mean age of the study group was 59.2 (+/-13.8), with a predominance of male patients (61.9%). 10.7% of our patient cohort had diabetes, and another 63.8% had post-operative radiation therapy. Patients with SSI were more likely to have plate exposure (25 vs. 6.4%, p <0.001). Post-operative SSI, mandibulectomy defects, and plate profile/thickness were associated with plate exposure on univariable analysis (OR = 5.72, p < 0.001; OR = 2.56, p = 0.014; OR = 1.44, p = 0.003 respectively) and multivariable analysis (OR = 5.13, p < 0.001; OR = 1.36, p = 0.017; OR = 2.58, p = 0.02 respectively). CONCLUSION: Surgical site infections are associated with higher rates of plate exposure. Plate exposure may require multiple procedures to manage and occasionally free flap reconstruction.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".