Usefulness of Cone-Beam Computed Tomography in Determining the Position of Ossicular Prostheses
Bibliographic record
Abstract
HYPOTHESIS: Cone-beam computed tomography (CT) is proving useful in various operative settings. We hypothesize that it has great potential as an intraoperative assessment tool for ossicular prosthesis positioning. BACKGROUND: Results from prosthetic ossiculoplasty are frequently disappointing. Undetected intraoperative displacement of the prosthesis may be caused, and obscured, by placement of an overlying cartilage graft. METHODS: A cadaveric right temporal bone was prepared with a tympanomeatal flap, and an extended posterior tympanotomy through a cortical mastoidectomy. Each of 3 commercially available prostheses was positioned in 3 different locations: (1) optimal, (2) grossly displaced, and (3) marginally displaced. The intended prosthetic positions were confirmed by endoscopy before and after cone-beam CT image acquisition. The primary outcome measure was the position of the prosthesis in relation to the stapes and tympanic membrane, as assessed by 5 expert reviewers blinded to the study. Secondary outcome measures included optimal dosing for adequate image resolution and radiographic scatter associated with different prosthetic materials. RESULTS: Cone-beam CT accurately demonstrated the position of ossicular reconstruction prostheses with respect to the stapes and tympanic membrane. Prosthesis displacement, whether minimally or marked, was also accurately demonstrated. Interobserver agreement among the 5 reviewers, measured using a Fleiss κ statistic, ranged from 0.4 to 0.8 (fair to substantial agreement depending on type and position of the prosthesis). CONCLUSION: Cone-beam CT is a useful tool for determining the position of ossicular reconstruction prostheses in situ. We suggest it has potential for intraoperative assessment, to check positioning after the prosthesis has been covered with a cartilage graft and tympanomeatal flap.
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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 | 0.020 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".