Background: Quantitative pupillometry is used in mild traumatic brain injury (mTBI) with changes in pupil reactivity noted after blast injury, chronic mTBI, and sports-related concussion.Objective: We evaluated the diagnostic capabilities of a smartphone-based digital pupillometer to differentiate patients with mTBI in the emergency department from controls.Methods: Adult patients diagnosed with acute mTBI with normal neuroimaging were evaluated in an emergency department within 36 hours of injury (control group: healthy adults). The PupilScreen smartphone pupillometer was used to measure the pupillary light reflex (PLR), and quantitative curve morphological parameters of the PLR were compared between mTBI and healthy controls. To address the class imbalance in our sample, a synthetic minority oversampling technique was applied. All possible combinations of PLR parameters produced by the smartphone pupillometer were then applied as features to 4 binary classification machine learning algorithms: random forest, k-nearest neighbors, support vector machine, and logistic regression. A 10-fold cross-validation technique stratified by cohort was used to produce accuracy, sensitivity, specificity, area under the curve, and F1-score metrics for the classification of mTBI versus healthy participants.Results: Of 12 patients with acute mTBI, 33% (4/12) were female (mean age 54.1, SD 22.2 years), and 58% (7/12) were White with a median Glasgow Coma Scale (GCS) of 15. Of the 132 healthy patients, 67% (88/132) were female, with a mean age of 36 (SD 10.2) years and 64% (84/132) were White with a median GCS of 15. Significant differences were observed in PLR recordings between healthy controls and patients with acute mTBI in the PLR parameters, that are (1) percent change (mean 34%, SD 8.3% vs mean 26%, SD 7.9%; P<.001), (2) minimum pupillary diameter (mean 34.8, SD 6.1 pixels vs mean 29.7, SD 6.1 pixels; P=.004), (3) maximum pupillary diameter (mean 53.6, SD 12.4 pixels vs mean 40.9, SD 11.9 pixels; P<.001), and (4) mean constriction velocity (mean 11.5, SD 5.0 pixels/second vs mean 6.8, SD 3.0 pixels/second; P<.001) between cohorts. After the synthetic minority oversampling technique, both cohorts had a sample size of 132 recordings. The best-performing binary classification model was a random forest model using the PLR parameters of latency, percent change, maximum diameter, minimum diameter, mean constriction velocity, and maximum constriction velocity as features. This model produced an overall accuracy of 93.5%, sensitivity of 96.2%, specificity of 90.9%, area under the curve of 0.936, and F1-score of 93.7% for differentiating between pupillary changes in mTBI and healthy participants. The absolute values are unable to be provided for the performance percentages reported here due to the mechanism of 10-fold cross validation that was used to obtain them.Conclusions: In this pilot study, quantitative smartphone pupillometry demonstrates the potential to be a useful tool in the future diagnosis of acute mTBI.

Background: Degenerative changes at the sesamoid-metatarsal joints (SMJs) may be a source of pain following hallux valgus surgery. The aims of this study were to describe degenerative changes at the SMJs on weightbearing computed tomography (WBCT) scans and, secondarily, investigate their association with 1-year patient-reported outcome scores following a modified Lapidus procedure for hallux valgus. We hypothesized that reduced joint space in the SMJs would correlate with worse patient-reported outcomes. Methods: Fifty-seven hallux valgus patients who underwent a modified Lapidus procedure had preoperative and minimum 5-month postoperative WBCT scans, and preoperative and at least 1-year postoperative PROMIS physical function (PF), pain interference, and pain intensity scores were included. Degenerative changes at the SMJs were measured using distance mapping between the sesamoids and first metatarsal head on preoperative and postoperative WBCT scans. The minimum and average distances between the first metatarsal head and tibial sesamoid (tibial-SMJ) for each patient preoperatively and postoperatively were measured. Sesamoid station was measured on WBCT scans using a 0 to 3 grading system. Linear regression was used to investigate the correlations between minimum preoperative and postoperative tibial-SMJ distances and 1-year postoperative PROMIS scores. Results: The median minimum and average tibial-SMJ distances increased from 0.82 mm (interquartile range [IQR] 0.40-1.03 mm) and 1.62 mm (IQR 1.37-1.75 mm) preoperative to 1.09 mm (IQR 0.96-1.23 mm) and 1.73 mm (IQR 1.60-1.91 mm) postoperative (P < .001 and P < .001), respectively. In a subset of patients with complete sesamoid reduction, we found an association between preoperative minimum tibial-SMJ distance and 1-year postoperative PROMIS PF scores (coefficient 7.2, P = .02). Conclusion: Following the modified Lapidus procedure, there was a statistically significant increase in the tibial-SMJ distance. Additionally, in patients with reduced sesamoids postoperatively, reduced preoperative tibial-SMJ distance correlated with worse PROMIS PF scores. Level of Evidence: Level IV, case series.

BACKGROUND Quantitative pupillometry is used in mild traumatic brain injury (mTBI) with changes in pupil reactivity noted after blast injury, chronic mTBI, and sports-related concussion. OBJECTIVE We evaluated the diagnostic capabilities of a smartphone-based digital pupillometer to differentiate patients with mTBI in the emergency department from controls. METHODS Adult patients diagnosed with acute mTBI with normal neuroimaging were evaluated in an emergency department within 36 hours of injury (control group: healthy adults). The PupilScreen smartphone pupillometer was used to measure the pupillary light reflex (PLR), and quantitative curve morphological parameters of the PLR were compared between mTBI and healthy controls. To address the class imbalance in our sample, a synthetic minority oversampling technique was applied. All possible combinations of PLR parameters produced by the smartphone pupillometer were then applied as features to 4 binary classification machine learning algorithms: random forest, k-nearest neighbors, support vector machine, and logistic regression. A 10-fold cross-validation technique stratified by cohort was used to produce accuracy, sensitivity, specificity, area under the curve, and F1-score metrics for the classification of mTBI versus healthy participants. RESULTS Of 12 patients with acute mTBI, 33% (4/12) were female (mean age 54.1, SD 22.2 years), and 58% (7/12) were White with a median Glasgow Coma Scale (GCS) of 15. Of the 132 healthy patients, 67% (88/132) were female, with a mean age of 36 (SD 10.2) years and 64% (84/132) were White with a median GCS of 15. Significant differences were observed in PLR recordings between healthy controls and patients with acute mTBI in the PLR parameters, that are (1) percent change (mean 34%, SD 8.3% vs mean 26%, SD 7.9%; P<.001), (2) minimum pupillary diameter (mean 34.8, SD 6.1 pixels vs mean 29.7, SD 6.1 pixels; P=.004), (3) maximum pupillary diameter (mean 53.6, SD 12.4 pixels vs mean 40.9, SD 11.9 pixels; P<.001), and (4) mean constriction velocity (mean 11.5, SD 5.0 pixels/second vs mean 6.8, SD 3.0 pixels/second; P<.001) between cohorts. After the synthetic minority oversampling technique, both cohorts had a sample size of 132 recordings. The best-performing binary classification model was a random forest model using the PLR parameters of latency, percent change, maximum diameter, minimum diameter, mean constriction velocity, and maximum constriction velocity as features. This model produced an overall accuracy of 93.5%, sensitivity of 96.2%, specificity of 90.9%, area under the curve of 0.936, and F1-score of 93.7% for differentiating between pupillary changes in mTBI and healthy participants. The absolute values are unable to be provided for the performance percentages reported here due to the mechanism of 10-fold cross validation that was used to obtain them. CONCLUSIONS In this pilot study, quantitative smartphone pupillometry demonstrates the potential to be a useful tool in the future diagnosis of acute mTBI. CLINICALTRIAL

Study Design: Retrospective cohort study. Objective: To quantify and compare the carbon footprint (CF) of open and MIS microdiscectomy. Methods: The study included 161 patients undergoing primary 1 level open or minimally invasive surgery (MIS) for lumbar disc herniation between 2018 and 2022. Life cycle assessment (LCA) modeling was used to calculate the CF produced during the distribution, use and waste phases of each item used during surgery. Independent samples t-tests and Chi square or Fischer’s exact tests were used to compare the CF of open and MIS procedures. Results: There were 161 cases (100 MIS, 61 Open). MIS had a significantly higher overall CF (114.67 ± 60.06 kg-CO ₂ e) vs open surgery (95.15 ± 45.45 kg-CO ₂ e, P = 0.021). Key contributors included disposable surgical supplies, energy-intensive OR equipment, and anesthetic gases. Only the waste phase had a significant difference in CF, with MIS producing more waste (MIS: 31.50 ± 25.67; Open: 18.18 ± 10.25; kg-CO ₂ e; P < 0.001) and consuming more energy, especially for heating, ventilation, and air conditioning systems and fluoroscopy. Conclusions: MIS microdiscectomy results in higher CF and environmental costs compared to open procedures, highlighting the need for sustainable practices, especially waste reduction, use of reusable materials and optimized energy use in ORs. A multifaceted approach is crucial to mitigate the environmental impact of spine surgeries and should be applied to both MIS and open techniques.

Study Design: Prospective Cohort Study. Objectives: The aim of this study was to assess how sound pressure levels (SPL) change throughout spine procedures and which phases may place surgeons at higher risk of noise induced hearing loss. Methods: SPL was recorded by a microphone attached to the surgeon’s collar beneath the sterile gown for 6 of each: MI-TLIF, microdiscectomy, decompression. dBA was recorded every second. An overall average (ADL) and maximum (MDL) for each phase of the procedure were calculated and compared across procedure type. The ADL and MDL of two electric burrs and one pneumatic burr were collected and compared. Results: ADLs were significantly different for all three procedure types. There was no difference in the mean MDL (mMDL) between phases within each procedure type. The irrigation/closure phase had the lowest ADL and mMDL for all three procedure types. In MI-TLIF, facetectomy had a significantly greater ADL than all other phases; the highest mMDL was during the exposure phase. In microdiscectomy, laminotomy had the greatest mMDL, and ADL and was significantly higher than all other phases except exposure. In decompression, the laminectomy phase had the highest ADL and mMDL. The brand A-e burr had a significantly lower ADL and the lowest MDL. Conclusion: During common spine surgery procedures, phases involving powered instruments such as burrs, electrocautery machines or suction had higher ADL and mMDL. Sound levels in the OR may peak above 85 dBA for short periods despite the average being below dangerous levels. Therefore, while surgeons are not usually at an elevated risk of NIHL, periods of elevated dBA warrant attention for elevated risk.