Patient management for cochlear implant recipients in audiology departments: A practice review
Bibliographic record
Abstract
OBJECTIVES: To determine and evaluate the time clinics needed to complete the sub-processes involved in the first-fitting and follow-up fitting of people with a cochlear implant. METHODS: Eight HEARRING clinics completed a questionnaire recording how long it took to complete the sub-processes involved in first-fitting and follow-up fitting cochlear implant recipients. The mean times of clinics and procedures were then compared. RESULTS: Questionnaires on 77 patients were completed. Clinics varied widely on time spent on each sub-process in both first- and follow-up fittings. Total first-fitting times were similar across clinics. Follow-up fitting times varied more across clinics although this may have been due to differences in questionnaire interpretation. DISCUSSION: If a patient management plan can help increasingly busy cochlear implant clinics provide high-quality care more efficiently, essential first steps are determining which procedures are generally performed and how long their performance takes. Until reliable data are gathered, constructing a patient management plan or reaping the potential benefits of its use will remain elusive; clinics will have to find what solutions they can to meet rising workload demands. CONCLUSION: The variation in time spent on each sub-process may suggest that some clinics have more efficient workflow procedures. Compiling a best practice for each process could be instrumental in creating a professional process management plan that would increase efficiency without sacrificing quality of care.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".