A biomechanical analysis of loads on x-ray technologists: a field study
Bibliographic record
Abstract
The aim of the study was to determine the biomechanical loads on radiographers in their occupation. Seven x-ray technologists (one male and six females with combined mean age 32.5 years, mean height 164.6 cm and mean weight 68.3 kg) volunteered to be videotaped while doing their jobs (same as normal and others in a simulated manner). X-ray technologists perform all these tasks regularly every day as part of their assignments. The magnitude of the load handled was measured on weighing scale and recorded. The videotape was played back in the laboratory and the initial and final frames of the tasks investigated were frozen for analysis. The joint angles, the height and weight of the technologist, and the load on their hands (weighed before the task was performed) were input into the 3-D Michigan static strength model for calculation of the lumbosacral load and loads on the joints of the extremities. The lumbosacral compression and shear loads were calculated for 16 tasks commonly performed by all x-ray technologists. The x-ray technologists' work was found to be biomechanically quite demanding. Tasks such as repositioning patients horizontally and lifting a patient from a wheelchair caused lumbosacral compression loads of 7,936N and 8,335N respectively, in which exceeded the maximum permissible limit set by NIOSH in 1981. The action limit defined by NIOSH was exceeded by other tasks. It is also of interest that the majority of the x-ray technologists were female.
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.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".