Establishing Safety Parameters for Orthopaedic Cast Saw Blade Usage
Bibliographic record
Abstract
BACKGROUND: The incidence of injuries from cast saws during cast removal ranges from 0.12% to 4.3%. With 1 second or less of exposure time, a temperature of 65°C can cause partial thickness burns. Despite numerous studies that recommend avoiding the use of a dull blade, there is no objective measure of what defines dullness. METHODS: Plaster and fiberglass casts were collected and measured after removal from patients in the clinic. A series of slabs were constructed based on these measurements. To simulate our emergency department setting, a Stryker 940 cast saw without an attached vacuum was used to split plaster slabs. A thermocouple was used to directly measure the 940-23 ion-nitride saw blade temperature after each use. To simulate our orthopaedic clinic setting, a Stryker 940 cast saw with an attached vacuum was used to split fiberglass and plaster slabs. Three blades were tested in each setting, bivalving 50 slabs each. RESULTS: For the plaster slabs split without a vacuum, average blade temperature of the 3 blades reached 65°C on the 42nd cast. However, the individual blades exceeded 65°C on the 33rd, 31st, and 38th casts, respectively. For the fiberglass and plaster slabs split with a vacuum, average blade temperature reached a maximum of 57.5°C in the first 50 trials. Extrapolating from this data, the blade is predicted to exceed 65°C on the 104th cast. CONCLUSIONS: When a Stryker 940 cast saw without vacuum is used to cut plaster casts, the ion-nitride blade should be changed frequently, at minimum after 60 casts have been split, or 30 casts have been bivalved. When a Stryker 940 cast saw with vacuum is used to remove fiberglass and plaster casts, the ion-nitride blade should be changed after removing 103 casts. A cast saw with an attached vacuum should be used whenever possible to minimize the risk of burning patients. CLINICAL RELEVANCE: Determine how often a cast saw blade should be changed to minimize risk of burning patients.
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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".