PRODIGY score predicts respiratory depression in the post-anesthesia care unit: A post-hoc analysis
Bibliographic record
Abstract
Surgical patients who experience respiratory depressive episodes (RDEs) during their post-anesthesia care unit (PACU) admission are at a higher risk of developing subsequent respiratory complications in general care wards. A risk assessment tool for PACU RDEs has not been previously assessed. The PRediction of Opioid-induced respiratory Depression In patients monitored by capnoGraphY (PRODIGY) score is an assessment tool that uses baseline patient variables to categorize patients into low, intermediate, or high risk groups for RDEs in general care wards. This study assessed whether PRODIGY groups are associated with PACU RDEs. This analysis utilized data from a previous observational trial of PACU RDEs detected by capnography. PRODIGY scores were retrospectively calculated, and the number and duration of respiratory alerts were compared among PRODIGY groups. Twenty-six (29.9%) patients were classified as low risk, 29 (33.3%) as intermediate risk, and 32 (36.8%) as high risk. A total of 3,580 alerts were recorded in the PACU, 47% of which were apnea episodes lasting ≥ 10 seconds. The total number and duration of alerts were highest in high risk group patients (median 56 [IQR 12 - 87] alerts per patient vs 22 [9 - 37] in low risk and 26 [13 - 42] in intermediate risk patients, P = 0.035; 303 [123 - 885] seconds vs 177 [30 - 779] in low risk and 301 [168 - 703] in intermediate risk patients, P = 0.042). Poisson regression analysis indicated that the rate of RDEs in the high PRODIGY risk group was higher than in the intermediate (rate ratio estimate = 2.01 [95% CI 1.86 - 2.18], P < 0.001) and low (rate ratio estimate = 2.25 [95% confidence interval 2.07 - 2.45], P < 0.001) risk groups. This analysis suggests that the PRODIGY score may be useful in assessing the risk of PACU RDEs. Trial Registration: https://www.clinicaltrials.gov/ct2/show/NCT02707003.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".