Bibliographic record
Abstract
BACKGROUND: Standard indications for endotracheal suctioning are often based on clinical judgment on the deterioration of the patient9s condition, and/or routine suctioning. TBA Care is a secretion detector that analyses airway sounds and indicates the need for suctioning. OBJECTIVE: To determine the efficacy of TBA Care in detecting retained secretions, compared to standard indications. METHODS: We conducted a prospective randomized trial with 72 general intensive care unit patients randomized at intubation into 2 groups, differing only in suctioning indications. The control group indications were at least 3 scheduled suctionings per day or were clinically driven. The secretion-detector group indications were device signal or clinically driven. At each suctioning session we recorded the indication for suctioning and the amount of secretions removed. Patients were followed until intensive care unit discharge or extubation. Diagnosis of ventilator-associated pneumonia was confirmed via microbiological analysis of suctioned secretions. RESULTS: We analyzed 1,705 suctionings in the control group and 1,354 in the secretion-detector group. The secretion-detector group had fewer suctionings per day (3.9 ± 2.3 vs 4.8 ± 1.2, P = .002) and a lower rate of unnecessary suctionings (4% vs 12%, P < .001). In the secretion-detector group, 97% of the suctionings were performed following the signal from the TBA Care device. In the control group, clinical deterioration (65%) was the most frequent indication for suctioning. The incidence of ventilator-associated pneumonia was similar in the groups. CONCLUSIONS: TBA Care seems to give valid and timely indications for suctioning, anticipating clinical deterioration due to secretion retention and reducing unnecessary suctionings. (ClinicalTrials.gov registration NCT00932776.)
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.866 | 0.636 |
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".