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.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".