A Novel 3D-Printed Head Holder for Guinea Pig Ear Surgery
Bibliographic record
Abstract
BACKGROUND: Otologic surgery in guinea pig requires head immobilization for microscopic manipulation. Existing commercially available stereotaxic frames are expensive and impede access to the ear as they rely on ear bars or mouthpieces to secure the head. METHOD: Prototype head holders were designed using the Solidworks 2019 software and 3D-printed using Formlabs Form 2 Printers with photopolymer resin. The head holder consists of a C-shaped brace with adjustable radial inserts of 1/4-20 UNC standard screws with cone point tips providing head fixation for animals of various sizes. The C-shaped brace is attached to a rod that can be secured to a commercially available micromanipulator. The head holder design was tested during in vivo guinea pig experiments where their head motion with (n = 22) and without the head holder (n = 2) was evaluated visually through a stereotaxic microscope at 24× magnification during surgery. RESULTS: The head holder design was easy to use and allowed for both nose cone administration of anesthesia and access to the ear for intraoperative auditory testing and manipulation. Functionally, the head holder successfully minimized head movement. Furthermore, harvested round window membranes evaluated at 72 hours following surgery showed precise perforations with the use of head holder. CONCLUSION: The novel 3D-printed head holder enables simultaneous access for nose cone administration of anesthesia and surgical manipulation of the ear and brain. Moreover, it provides a modular, intuitive, and economical alternative to commercial stereotaxic devices for minimizing head motion during small animal surgery.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".