SLEEP-GOAL: A multicenter success criteria outcome study on 302 obstructive sleep apnoea (OSA) patients.
Bibliographic record
Abstract
OBJECTIVE: To demonstrate SLEEP-GOAL as a more holistic and comprehensive success criterion for Obstructive Sleep Apnoea (OSA) treatment. METHODS: A prospective 7-country clinical trial of 302 OSA patients, who met the selection criteria, and underwent nose, palate and/or tongue surgery. Pre- and post-operative data were recorded and analysed based on both the Sher criteria (apnoea hypopnea index, AHI reduction 50% and <20) and the SLEEP-GOAL. RESULTS: There were 229 males and 73 females, mean age of 42.4±17.3 years, mean BMI 27.9±4.2. The mean VAS score improved from 7.7±1.4 to 2.5±1.7 (p<0.05), mean Epworth score (ESS) improved from 12.2±4.6 to 4.9±2.8 (p<0.05), mean body mass index (BMI) decreased from 27.9±4.2 to 26.1±3.7 (p>0.05), gross weight decreased from 81.9±14.3kg to 76.6±13.3kg. The mean AHI decreased 33.4±18.9 to 14.6±11.0 (p<0.05), mean lowest oxygen saturation (LSAT) improved 79.4±9.2% to 86.9±5.9% (p<0.05), and mean duration of oxygen <90% decreased from 32.6±8.9 minutes to 7.3±2.1 minutes (p<0.05). The overall success rate (302 patients) based on the Sher criteria was 66.2%. Crosstabulation of respective major/minor criteria fulfilment, based on fulfilment of two major and two minor or better, the success rate (based on SLEEP-GOAL) was 69.8%. Based solely on the Sher criteria, 63 patients who had significant blood pressure reduction, 29 patients who had BMI reduction and 66 patients who had clinically significant decrease in duration of oxygen <90% would have been misclassified as "failures". CONCLUSION: AHI as a single parameter is unreliable. Assessing true success outcomes of OSA treatment, requires comprehensive and holistic parameters, reflecting true end-organ injury/function; the SLEEP-GOAL meets these requirements.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".