Detection of Modern Spinal Implants by Airport Metal Detectors
Bibliographic record
Abstract
STUDY DESIGN: Prospective cohort observational study. OBJECTIVE: To determine the detection rates of modern spinal implants by post-9/11 airport metal detectors. SUMMARY OF BACKGROUND DATA: There are few data on the detection rates of modern spinal implants and few that examine the effects of body mass, construct complexity, or implant density. METHODS: Implants were tested ex vivo and in vivo using standard arch way metal detectors (AMDs) and handheld metal detectors (HHMDs) in use at the majority of European airports.A volunteer carried individual spinal implants both individually and in various combinations of increasing mass in clothing pockets into the AMD. The same instrumentation was bench tested using HHMD at a distance of 5 cm. Forty patients with modern spinal implants were tested: lumbar disc replacement (8), cervical disc replacement (1), posterior deformity instrumentation (17), anterior deformity instrumentation (2), anterior reconstruction (2), PLIF (6), interspinous distraction device (1), anterior cervical plate (2), and anterior lumbar interbody fusion with cage (1)-all implants were titanium unless indicated. Mean metal mass was 98 g (range, 6-222 g). Subject ages ranged from 13 to 65 years and the mean body mass index was 25 kg/m (range, 15-32). RESULTS: Ex vivo, the AMD did not detect any instrumentation individually or in combination up to a titanium mass of 215 g. The HHMD detected all instrumentation at a distance of 5 cm, with the minimum mass being 2 g. No implants were detected in patients by the AMD.The HHMD did not detect any anterior lumbar or thoracic surgical implants. It detected anterior cervical implants. The HHMD detected all posterior surgical implants. There was no significant relationship between detection rate, body mass index, total metal mass, and metal density/segment. CONCLUSION: AMDs do not detect modern spinal implants. HHMDs detect all modern posterior spinal implants; this has implications for patient documentation.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".