Bibliographic record
Abstract
In the letter to the editor titled “STOP Questionnaire Warrants CAUTION Sign,” Dr. Overdyk and colleagues raised some interesting issues regarding the apnea-hypopnea index (AHI) and our papers.With the variation between different sleep centers, and even different nights for the same patient at the same sleep center,1,2there have been different opinions regarding the definition and interpretation of the AHI. Considering the feasibility and cost, we chose the result of one night of in-laboratory polysomnography as the gold standard to validate the STOP questionnaire. When we started our study in early 2006, there were no widely accepted mandatory agreements on the definition of the different measurements in polysomnography. The majority of sleep laboratories followed the recommendations of the Academy of Sleep Medicine Task Force, published in 1997.3The Academy of Sleep Medicine has since published a new manual for the scoring of sleep and associated events in 2007.4The new manual is mandatory to be followed in United States after July 1, 2008.*Comparing the criteria that we used with the 2007 manual, there is no difference regarding the definition of apnea. However, there are some changes in the definition of hypopnea. In the criteria we used, an episode of hypopnea was defined as a reduction of nasal air flow ≥ 50% with a drop in pulse oxygen saturation ≥ 3% which last more than 10 s. In the 2007 manual, either a drop of pulse oxygen saturation ≥ 4% with a decrease of nasal air flow ≥ 30% or a drop of pulse oxygen saturation ≥ 3% with a decrease of nasal air flow ≥50% which last ≥ 10 s will be defined as 1 episode of hypopnea. The definition in the 2007 manual for hypopnea is broader than the definition we used for our study.In our 177 study patients for validating the STOP questionnaire, the severity classification based on the AHI and number of patients in each group can be found on page 817;5AHI ≤ 5:55, AHI > 5 and ≤ 15:52, AHI > 15 and ≤ 30:31, and AHI > 30:39. When doing the analysis of predictive parameters, we had to classify patients into either smaller or bigger than the cutoff value and use this classification to evaluate the screening tools. That is the reason why we combined patients with moderate and severe obstructive sleep apnea (OSA) in one group to evaluate the capacity of screening tools to identify this group of patients.We agree with Dr. Overdyk and colleagues that the duration of oxygen desaturation, apnea and hypopnea, rate of desaturation, adequacy of ventilation recovery, and level and stability of the arousal threshold are very important factors in evaluating the severity of OSA, especially for assessing the potential to trigger other perioperative adverse events. However, there is no agreement yet on how to incorporate these factors into the severity classification of OSA patients.Our main focus was to develop and validate a concise and easy-to-use screening tool for preoperative clinics. We agree with Dr. Overdyk and colleagues that the STOP questionnaire is a practical step forward in identifying patients with OSA, and it bears the same limitations as other questionnaires. To more accurately stratify the perioperative risk, guide postoperative monitoring, and predict outcome, we need to combine the score of the STOP questionnaire with the other information such as the need for narcotics and the invasiveness of the surgery. These points were illustrated in the American Society of Anesthesiologists guideline on the perioperative management of OSA patients.6†University of Toronto, Toronto Western Hospital, University Health Network, Toronto, Ontario, Canada. Frances.Chung@uhn.on.ca
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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 | 0.008 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.015 | 0.008 |
| Insufficient payload (model declined to judge) | 0.099 | 0.083 |
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".