Surgical site infections following oral cavity cancer resection and reconstruction is a risk factor for plate exposure
Why this work is in the frame
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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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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 it