Evaluating the efficiency and effectiveness of approaches to nasogastric tube insertion during trauma care
Bibliographic record
Abstract
BACKGROUND: Numerous invasive, uncomfortable, and discomforting procedures are implemented almost routinely during trauma care. Previous research has shown that trauma care practitioners use comforting strategies during this care. Yet little is known of the effect of these comforting strategies on the effectiveness and efficiency of treatment. OBJECTIVES: To evaluate the effect of the caregiver's approach on the efficient and effective completion of a discomforting procedure (nasogastric tube insertion) on conscious patients during trauma care. METHODS: Ethology was used to analyze 32 attempts at nasogastric tube insertion from 193 videotaped trauma cases from 3 level I trauma centers in North America. Both qualitative and quantitative analytic techniques were used. RESULTS: The practitioner's approach was associated with the outcome of the treatment. Overall, practitioners who balanced the technical aspects of the procedure with use of comforting strategies to minimize the patient's discomfort (the blended approach) were most efficient and most effective in completing this procedure. Practitioners who were most attentive to procedural technique (with little respect to patients' discomfort) or who were overly attentive to comforting strategies (termed the technical and affective approaches, respectively) took longer and/or were less successful at completing the procedure. CONCLUSIONS: Four patterned, standardized approaches to care were found: technical, affective, blended, and mixed. This study has implications for further research into the effect of the practitioner's approach on the patient's behavioral state in trauma care.
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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 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".