Establishing Usage Patterns and Repair Costs for Video Rhinolaryngoscopes at a Tertiary Academic Outpatient Center
Bibliographic record
Abstract
OBJECTIVE: To evaluate the financial impact of video rhinolaryngoscope repairs by determining repair costs and assessing the link between reprocessing patterns and repair frequency. STUDY DESIGN: Retrospective review. SETTING: Outpatient settings at two tertiary care academic centers. METHODS: Repair and maintenance records for video rhinolaryngoscopes were analyzed for two tertiary care academic centers, Hospital V and Hospital S. Data were collected from January 1, 2021, to March 1, 2024, for Hospital S, and from June 18, 2019, to March 1, 2024, for Hospital V. Hospital S utilized automated endoscope reprocessing, whereas Hospital V employed manual reprocessing. Both hospitals used Olympus flexible video rhinolaryngoscopes. RESULTS: Hospital V reprocessed the endoscopes within the clinic space, whereas Hospital S used centralized reprocessing. The age of rhinolaryngoscopes varied at Hospital S, whereas all endoscopes were purchased new at Hospital V during the time of study. Hospital V, with 11 rhinolaryngoscopes, conducted 15,776 outpatient rhinolaryngoscopy examinations, averaging 435 uses per endoscope annually. Only one endoscope required repair, with a total cost of CAD $1940, resulting in a repair cost of CAD $0.12 per examination. In contrast, Hospital S, operating with 17 rhinolaryngoscopes, performed 7812 exams, averaging 145 uses per endoscope annually. A total of 28 repair instances were reported, with a total cost of CAD $87,950, resulting in a repair cost of CAD $11.26 per examination. CONCLUSION: This study highlights the impact of equipment age and reprocessing practices on repair costs and frequencies for reusable video rhinolaryngoscopes. The repair costs at both hospitals are supportive of continued use of reusable rhinolaryngoscopes.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".