X-ray vision: the accuracy and repeatability of a technology that allows clinicians to see spinal X-rays superimposed on a person's back
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: Since the discovery of ionizing radiation, clinicians have evaluated X-ray images separately from the patient. The objective of this study was to investigate the accuracy and repeatability of a new technology which seeks to resolve this historic limitation by projecting anatomically correct X-ray images on to a person's skin. METHODS: A total of 13 participants enrolled in the study, each having a pre-existing anteroposterior lumbar X-ray. Each participant's image was uploaded into the Hololens Mixed reality system which when worn, allowed a single examiner to view a participant's own X-ray superimposed on the participant's back. The projected image was topographically corrected using depth information obtained by the Hololens system then aligned via existing anatomic landmarks. Using this superimposed image, vertebral levels were identified and validated against spinous process locations obtained by ultrasound. This process was repeated 1-5 days later. The projection of each vertebra was deemed to be "on-target" if it fell within the known morphological dimensions of the spinous process for that specific vertebral level. RESULTS: The projection system created on-target projections with respect to individual vertebral levels 73% of the time with no significant difference seen between testing sessions. The average repeatability for all vertebral levels between testing sessions was 77%. CONCLUSION: These accuracy and repeatability data suggest that the accuracy and repeatability of projecting X-rays directly on to the skin is feasible for identifying underlying anatomy and as such, has potential to place radiological evaluation within the patient context. Future opportunities to improve this procedure will focus on mitigating potential sources of error.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it