How Mechanical Ventilation Measurement, Cutoff and Duration Affect Rapid Shallow Breathing Index Accuracy: A Randomized Trial
Bibliographic record
Abstract
BACKGROUND: Decreased accuracy of the rapid shallow breathing index (RSBI) can stem from 1) the method used to obtain this index, 2) duration of mechanical ventilation (MV), and 3) the established cutoff point. The objective was to evaluate the values of RSBI determined by three different methods, using distinct MV times and cutoff points. METHODS: O, and 100% tube compensation) (RSBI_ATC), and disconnected MV (RSBI_SP). The results were analyzed according to the MV period (less than or over 72 h) and to the outcome of extubation (< 72 h, successful and failed; > 72 h successful and failed). The accuracy of each method was determined at different cutoff points (105, 78, and 50 cycles/min/L). RESULTS: The RSBI_MIN, RSBI_ATC, and RSBI_SP values in the group < 72 h were 38 ± 18, 45 ± 26 and 55 ± 22; in the group > 72 h, RSBI_SP value was higher than those of RSBI_ATC and RSBI_MIN (78 ± 29, 51 ± 19 and 39 ± 14) (P < 0.001). For patients with MV > 72 h who failed in removing MV, the RSBI_SP was higher (93 ± 28, 58 ± 18 and 41 ± 10) (P < 0.000), with greater accuracy at cutoff of 78. CONCLUSION: RSBI_SP associated with cutoff point < 78 cycles/min/L seems to be the best strategy to identify failed extubation in subjects with MV for over 72 h.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".