The Role of Clinical Characteristics in Stratifying Sedation Risk: A Cohort Study
Bibliographic record
Abstract
BACKGROUND: Determination of sedation type during gastrointestinal procedures is generally based on risk assessment via the American Society of Anesthesiologists (ASA) classification system, but the reliance of anesthesia risk on clinical factors remains largely uninvestigated. We aim to determine the association between various clinical factors and choice of sedation type during gastrointestinal procedures. METHODS: This single-center, retrospective cohort study used electronic medical records to identify patients receiving colonoscopy or endoscopy at Rhode Island Hospital. The electronic medical record was queried for history of alcohol abuse, opioid abuse, polysubstance abuse, prescriptions for psychotropic or opioid medications and ASA classification. Logistic regression was used to measure how patient characteristics correlated with sedation type. RESULTS: Totally, 2,033 patients were included in the study; 1,080 patients received moderate sedation and 853 received monitored anesthesia care (MAC). Three hundred fifty-four (60.2%) MAC patients had a history of alcohol abuse compared to 234 (39.8%) moderate sedation patients (P < 0.2334); 178 (62.9%) MAC and 105 (37.1%) moderate sedation patients had a history of opioid abuse (P < 0.001); 203 (73.6%) MAC and 73 (26.4%) moderate sedation patients had a history of polysubstance abuse (P < 0.001); and 815 (75.1%) MAC patients had psychiatric comorbidities versus 270 (24.9%) in the moderate sedation group (P < 0.001). In the MAC cohort, alcohol, opioid, polysubstance abuse and psychiatric history were associated with previous failure of moderate sedation (P < 0.0001). CONCLUSIONS: For a subset of patients, clinical factors including alcohol, opioid, polysubstance abuse and psychiatric history, in addition to ASA classification, play an important role in sedation management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".