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Record W4255018517 · doi:10.1213/ane.0000000000000459

In Response

2015· letter· en· W4255018517 on OpenAlexaffabout
Karen A. Brown, Robert T. Brouillette

Bibliographic record

VenueAnesthesia & Analgesia · 2015
Typeletter
Languageen
FieldMedicine
TopicObstructive Sleep Apnea Research
Canadian institutionsMcGill UniversityMcGill University Health CentreMontreal Children's Hospital
Fundersnot available
KeywordsMedicinePerioperativeObstructive sleep apneaIntensive care medicinePulse oximetryPediatricsEmergency medicineAnesthesia

Abstract

fetched live from OpenAlex

We agree with Dr. Benumof1 that adult patients with severe obstructive sleep apnea (OSA) are also at risk for death while recovering from surgery and applaud the development of the OSA Death and Near Miss Registry for patients older than 17 years. The inclusion of children and adolescents in this registry might help determine the relative size of the adult and pediatric OSA “elephants.” Dr. Benumof’s prototypical case1 and many of the cases of death and neurologic injury reported by Coté et al.2 were suspected of having OSA, were given opioids, and were discharged from the recovery room to an unmonitored environment. By contrast, practice guidelines recommend that these patients should not be discharged unmonitored from the recovery area.3,4 Practicing anesthesiologists overwhelmingly agree that “hospitalized patients who are at increased risk of respiratory compromise from OSA should have continuous pulse oximetry monitoring after discharge from the recovery room” and recognize that the administration of continuous opioids constitutes a risk in the patient with severe OSA.4 Coté et al.2 speculate that clinical judgment might be clouded by performance pressure. Measures to prevent perioperative respiratory compromise, however, will only be taken if high-risk status is recognized. For some patients, OSA may not be diagnosed, and for others, the severity of OSA may go unrecognized without laboratory testing for sleep-disordered breathing. Competent clinicians can identify morbid obesity, genetic, neurologic, craniofacial, and other comorbidities that place patients at risk for perioperative respiratory compromise. Systematic reviews have consistently reported that in children, however, the accurate diagnosis of OSA and the assessment of the severity of OSA require that clinical judgment be supplemented by sleep laboratory testing.5,6 We have developed a cost-effective strategy to mitigate perioperative risk in children undergoing adenotonsillectomy—the McGill Oximetry Score. This system supplements a careful clinical assessment with home nocturnal pulse oximetry performed by the parents. Compared with polysomnography, this cost-effective strategy reduces sleep laboratory technician time 10-fold, identifies children at greatest risk for perioperative respiratory complications, prioritizes the surgical intervention, and informs anesthetic management.7,8 The McGill Oximetry Score has yet to be tested in a randomized controlled trial. Further validation including the comparison of our approach with clinical judgment alone is needed. Karen A. Brown, MD Department of Pediatric Anesthesia Montreal Children’s Hospital McGill University Health Centre Montreal, Quebec, Canada [email protected] Robert T. Brouillette, MD Department of Pediatrics McGill University Montreal, Quebec, Canada

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.051
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.333
Threshold uncertainty score0.951

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.051
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0050.004
Open science0.0030.004
Research integrity0.0130.014
Insufficient payload (model declined to judge)0.3330.168

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.

Opus teacher head0.031
GPT teacher head0.313
Teacher spread0.282 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
Domainnot available
GenreCommentary

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".

Quick stats

Citations0
Published2015
Admission routes2
Has abstractyes

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