Changes to a pediatric sleep disordered breathing clinic improve wait-times and clinic efficiency
Bibliographic record
Abstract
BACKGROUND: Recognition of the impact of sleep disordered breathing (SDB) on health has increased referrals in pediatric respiratory medicine with a concomitant increase in wait-times. METHODS: To reduce wait-time (primary outcome), we developed a rapid SDB clinic (RSC) to identify, diagnose, and treat patients with few to no comorbidities (uncomplicated) and presumed SDB based on the referral letter. The RSC uses 1) parent-report questionnaires to capture the patients' medical history and 2) sleep testing (e.g., overnight oximetry) completed prior to the initial consultation. RESULTS: The combination of pre-clinic electronic-questionnaires and testing increased patient consult capacity by 100%. Of the 256 patients referred to the RSC over 28 months, 130 patients were seen through the RSC, 17 patients were re-triaged to a standard sleep clinic (SSC) after questionnaire review, 51 patients were completing the RSC process, and 75 patients had their referral cancelled. An electronic-questionnaire RSC (n = 45) reduced wait-times by 34% to 142.8 (SD 57) days compared to a paper-questionnaire RSC (P < 0.001). The electronic RSC was also associated with 77.4 (SD 74.1) days reduction in wait-time (P = 0.04) for SSC patients seen during the same timeframe. RSC patients were 75% less likely to require a follow-up visit (P < 0.001) compared to SSC patients seen during the same timeframe. CONCLUSION: A targeted, streamlined clinic using electronic-questionnaires for uncomplicated patients can improve wait-times for children being referred to pediatric respiratory medicine for evaluation of sleep disordered breathing. Pediatr Pulmonol. 2016;51:1234-1241. © 2016 Wiley Periodicals, Inc.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".