Respiratory rates observed over 15 and 30 s compared with rates measured over 60 s: practice-based evidence from an observational study of acutely ill adult medical patients during hospital admission
Bibliographic record
Abstract
BACKGROUND: Respiratory rate is often measured over a period shorter than 1 min and then multiplied to produce a rate per minute. There are few reports of the performance of such estimates compared with rates measured over a full minute. AIM: Compare performance of respiratory rates calculated from 15 and 30 s of observations with measurements over 1 min. DESIGN: A prospective single center observational study. METHODS: The respiratory rates calculated from observations for 15 and 30 s were compared with simultaneous respiratory rates measured for a full minute on acutely ill medical patients during their admission to a resource poor hospital in sub-Saharan Africa using a novel respiratory rate tap counting software app. RESULTS: There were 770 respiratory rates recorded on 321 patients while they were in the hospital. The bias (limits of agreement) between the rate derived from 15 s of observations and the full minute was -1.22 breaths per minute (bpm) (-7.16 to 4.72 bpm), and between the rate derived from 30 s and the full minute was -0.46 bpm (-3.89 to 2.97 bpm). Rates observed over 1 min that scored 3 National Early Warning Score points were not identified by half the rates derived from 15 s and a quarter of the rates derived from 30 s. CONCLUSION: Practice-based evidence shows that abnormal respiratory rates are more reliably detected with measurements made over a full minute, and respiratory rate measurement 'short-cuts' often fail to identify sick patients.
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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 | 0.044 | 0.257 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".