Manual Shunt Connector Tool to Aid in No-Touch Technique
Bibliographic record
Abstract
BACKGROUND: Given the morbidity and cost associated with cerebrospinal fluid shunt infections, many neurosurgical protocols implement "no-touch" technique to minimize infection. However, current surgical tools are not designed specifically for this task and surgeons often resort to using their hands to connect the shunt catheter to the valve. OBJECTIVE: To develop an efficient and effective shunt assembly tool. METHODS: Prototypes were designed using computer assisted software and machined in stainless steel. The amount of time and number of attempts it took volunteers to connect a Bacticel shunt catheter to a Delta valve were recorded using the new tool and standard shodded mosquitos. Scanning electron microscopy (SEM) was done on manipulated catheters to assess potential damage. Practicing neurosurgeons provided feedback. RESULTS: Nonsurgeon (n = 13) volunteers and neurosurgeons (n = 6) both completed the task faster and with fewer attempts with the new tool (mean 7.18 vs 15.72 s and 2.00 vs 6.36 attempts, P < .0001; mean 2.93 vs 5.96 s and 1.06 vs 2.94 attempts, P < .001, respectively). SEM of 24 manipulated catheters showed no microscopic damage. 100% of neurosurgeons surveyed (n = 10) would adapt the tool in their practice, 90% preferred use of the new tool compared to their existing method, and 100% rated it easier to use compared to existing instruments. CONCLUSION: The new tool shortened the time and number of attempts to connect a shunt catheter to a valve. Neurosurgeons preferred the new tool to existing instruments. There was no evidence of catheter damage with the use of this tool.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".