Efficacy of powered mechanical lifting devices to minimize loads to the lower back
Bibliographic record
Abstract
Mechanical lifting devices (MELD) are recommended as an important intervention for reducing lifting injuries among nursing personnel. The literature evaluating MLD suggests that spinal loads are not minimized for all device types. A laboratory study was undertaken to evaluate the efficacy (or ability to minimize spinal loads) of powered MLD with respect to: (1) spinal compression forces relative to the NIOSH Action Limit of 3400 N, (2) literature estimates of spinal loads sustained while performing the transfer task manually (one-person/two person), and (3) a standard device, Hoyer lift. The primary outcomes of the study were spinal compression, anterior shear (across different loading conditions), and ratings of perceived exertion (RPE). An Extra-Period Latin Square design was employed to evaluate five powered MLD (three Floor and two Overhead types) and five registered Nurses while performing the heavy transfer task, from bed to chair. A single patient subject was selected and trained to remain passive throughout the transfer task (simulating a totally passive patient). Three dimensional position data (OPTOTRAK) and ground reaction forces (AMTI forceplates) were inputs for the spinal model. An inverse dynamic approach was used to calculate the net joint forces and moments about the L5/S1 spinal level. The transfer was partitioned into seven distinct phases for biomechanical analysis. The within Nurse test-re-test reliability for the compression outcomes was high for the majority of phases and loading conditions indicating the nurse was consistent in performing the transfer task. The NIOSH limit was exceeded for 20% of the trials and all Nurses exceeded the limit with the use of Floor MLD. The mean compression values were lower than reported estimates of performing this transfer manually, suggesting that MLD are efficacious. Efficacy with respect to a standard device varied as a function of the phase of the transfer. In general, Overhead Devices were found to be efficacious relative to the standard and had decreased forces for the whole transfer. Floor devices were efficacious for some outcomes in some phases of the transfer task. The results of this study have implications for the use and selection of MLD as part of a strategy to reduce back injuries.
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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 | 0.000 | 0.001 |
| 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) | 0.006 | 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".