Assessing the Use of Patient-Reported Outcome Measures in the Routine Clinical Care of Chronic Rhinosinusitis Patients: A Canadian Perspective
Bibliographic record
Abstract
Importance Chronic rhinosinusitis (CRS) is a common inflammatory disease of the paranasal sinuses with significant quality of life impairments. There is a need to implement outcome-based metrics to evaluate the outcomes of CRS treatment with endoscopic sinus surgery or biologics. Objective We aimed to understand Canadian otolaryngologists’ opinions on patient-related outcome measures (PROM) for CRS and identify potential barriers to implementation. Design Qualitative research. Setting and Participants A cross-sectional survey was distributed via the Canadian Society of Otolaryngology-Head and Neck Surgery and direct emailing. Measures Participants’ demographics, practice information, and opinions on PROM were collected. Results Of 346 (23%) Canadian otolaryngologists, 78 responded to the survey (26 rhinology fellowship–trained, 51 non-fellowship-trained, and 1 missing data). Thirty-eight responded that they collect PROM (69% with fellowship-trained, 39% non-fellowship-trained, P = .029). Regarding opinions on PROM, 74% of respondents agreed that it helps patients report their symptoms, 42% agreed that it improves the efficiency of the patient encounter, 54% agreed that it is easy for patients to understand, 62% agreed that it improves management and monitoring of clinical outcomes, and 71% disagreed that PROM is not helpful. Fellowship-trained otolaryngologists were 4 times more likely to agree that PROM improves management and monitoring of clinical outcomes ( P = .014), and no other differences in opinions were significant. The most-frequently-identified barriers to PROM usage were lack of time for 67% of respondents, difficulty integrating into clinical workflow for 64%, and lack of integration into the electronic medical record for 47%. If these barriers were addressed, 86% of respondents said they would use PROM in their practice. Conclusions and Relevance Despite the low uptake of PROM among otolaryngologists without rhinology fellowship, opinions were generally favorable. We identified barriers that, if addressed, may increase their use in clinical practice. As resource-limited therapies such as biologics become more prevalent in CRS management, PROM may find more applications in shared clinical decision making.
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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 | 0.050 | 0.114 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".