Reply to
Bibliographic record
Abstract
Editor, We have read with interest the comments and remarks made by Nijs1 and are thankful for the input. We agree that a multifaceted approach would ensure the best results. Darbyshire and Young2 suggested that there may be ‘technological solutions that could be used alongside awareness programmes to lower sound levels by more than that which can be achieved by behavioural interventions alone’ which is the intervention we carried out. We used a technological system, the SoundEar, to alert and let staff know about noise levels in the environment so that they would modify their behaviour. Focusing on Dr Nijs’ first issue, we acknowledge that placing the sensor in the centre of the ICU is not ideal. However, open plan ICUs seem to have a very homogenous acoustic reality, except where the unit architectually has isolation systems.2,3 The second issue that was raised refers to the Hawthorne effect of attention bias. Garrido Galindo et al.4,5 have demonstrated that such an effect does not usually last longer than a day. ICU settings may have a highly variable behaviour, so we chose a 2-week period because shorter measurement periods might not represent a true average. This length of time is similar or even longer than that which has been shown in the literature (between 3 days and 2 weeks).6 Therefore, we believe the measurements reported during each period by the sound level meter showed the acoustic reality of our ICU. The third issue refers to the ‘challenging classification of light alarms’. Background noise in our unit was between 52 and 55 dBA.7 Noise below this level would be practically impossible to achieve. Due to this, if we established more challenging noise classifications of the alarms, the SoundEar would be flashing orange or red continuously and could generate a detrimental effect by causing frustration among caregivers. As Nijs1 points out correctly, what truly matters is whether noise reduction has a positive effect on patient outcomes. Our study aimed to determine how a single, easily reproducible strategy would affect noise levels in a surgical ICU. Our results support a sustained reduction in noise both during and after the implementation of the SoundEar. However, our long-term objective would be to minimise noise levels as much as possible and we are thankful for the suggestions for further interventions such as awareness and staff education programmes to lower sound levels. Acknowledgements relating to this article Assistance with the letter: none. Financial support and sponsorship: none. Conflicts of interest: none.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".