In Reply
Bibliographic record
Abstract
We appreciate Dr. Kaw’s interest in our article,1 but we believe our interpretation of the data respected the study’s limitations, offering valid new insight on postoperative outcomes in patients with obstructive sleep apnea (OSA). We will respond in turn to the four issues raised by Dr. Kaw.The sensitivity and specificity of administrative data to clinical events vary by diagnosis.3 We can only hypothesize that a diagnosis of cardiac arrest and shock was the most frequently documented cardiovascular complication in both patients with OSA and their controls because it was more consistently detected and/or documented in the discharge abstract than acute coronary syndrome or atrial fibrillation, particularly, at the time the data were collected (1987–2008). Differences in the availability of cardiac troponin assays, the use of postoperative telemetry, and the range of included surgeries may explain the different rates of these complications between our study and another administrative database.2 Finally, the biologic plausibility of increased risk of cardiac arrest in patients with untreated OSA that Dr. Kaw is seeking can be found in the third last paragraph of the article.In summary, by linking polysomnography and administrative data, we created a large, unique database of postoperative outcomes in patients with OSA, from a time before routine preoperative screening and intensive postoperative monitoring. We carefully planned our study to address the limitations of administrative data and maximize its clinical applicability. It addressed important research questions that have eluded previous clinical studies for lack of statistical power4 and previous large administrative database studies for lack of polysomnography data.2 The results were cautiously interpreted within the limitations of the data and can help strengthen and refine current guidelines,5 with the goal of improving postoperative outcomes for patients with OSA.Dr. Kryger is a volunteer board member with the National Sleep Foundation (Arlington, Virginia). He has received research grants from Respironics, Inc. (Murrysville, Pennsylvania), ResMed Corp. (San Diego, California), and Dymedix Diagnostics Inc. (Shoreview, Minnesota) that were not used to fund this research. Since this research has been completed, he has received consultancy fees from Inspire Medical Systems Inc. (Maple Grove, Minnesota), Ventus Medical Inc. (Belmont, California), Dymedix, Medtronic (Minneapolis, Minnesota), and Merck & Co., Inc. (Whitehouse Station, New Jersey). The other authors declare no competing interests.
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.
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 | 0.005 | 0.062 |
| 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 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.015 | 0.030 |
| Insufficient payload (model declined to judge) | 0.035 | 0.026 |
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".