Patient management for cochlear implant recipients in audiology departments: A practice review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it