Bibliographic record
Abstract
The previous letter from Belgium in this Journal was published in February 2020, preceding the month when Belgium went in lock-down for COVID-19. With its almost ~12 million inhabitants on a surface of ~31.000 km2, three official languages, one federal and five regional governments, Belgium had challenges of its own in managing the crisis. We are now far enough removed from this dreadful period to be able to discern what good came out of it, for instance in terms of practical developments in the realm of respiratory research. This letter is an account from the perspective of a non-medical respiratory research scientist in Belgium in 2024, the year in which we also celebrate 100 years of surrealism (Figure 1). What struck me most in the midst of the COVID crisis is that while most medical caregivers were giving their very best on the frontlines, non-medical personnel basically interrupted all research projects involving human testing, to stay home and somehow figure out a way to contribute from there. One such contribution that popped up in various networks involving Belgian universities and industry was the development of low-cost, easy-to-manufacture ventilators that could be produced locally in a short time span. This was happening against the backdrop of intensive care clinicians around the world still working out what the optimal ventilator settings for the management of COVID patients would be. Existing open source plans were selected as a starting point to avoid losing precious time by reinventing the wheel. For the initiative at my university (https://medicalrespirator.be/nl/), the Ambubag concept from Massachusetts Institute of Technology (MIT) was used. During the subsequent development phase, we benefited from the particular historical ties between Belgian Universities and the centre of excellence for lung mechanics research, Meakins-Christie Laboratories at McGill University in Montreal. Making optimal use of different time zones, ideas were exchanged daily across the Atlantic in a true spirit of open science, where engineers who were novices in the realm of mechanical ventilation could benefit from a unique array of expertise from researchers who had been directly or indirectly involved with the pioneering studies on lung mechanics in the past century. A very practical challenge was access to the materials for manufacturing the prototypes, and in the Brussels area it was the local Audi manufacturing plant that provided the windscreen motors and pressure sensors. The idea of using parts that could be sourced from the automotive industry was motivated by the fact that this made the project exportable to virtually any country. There was speedy progress, only hampered by time-consuming physical endurance testing of the ventilator bellows that needed to withstand extended periods of cyclic opening and closing. By mid-2020 however it became clear that the projected ‘immediate’ need for 10,000 ventilators never materialized. Nevertheless, the expertise shared and gained in the process was at least as important and some of the resulting prototypes were subsequently presented at the (virtual) Oscillometry Summer Seminar at McGill in 2020 (access via: https://www.regresearchnetwork.org/working-groups-committees/technologies-working-group/). Several of these ventilator projects were reoriented towards third world countries, where commercial ventilators as we know them in Belgium are a rarity, and where repair capability is a crucial hurdle. Turning back to respiratory research in Belgium, several of the ventilator developments led to ongoing research projects, for instance to investigate the potential of Individualized Shared Ventilation1 or to improve the monitoring capabilities of ventilators by measuring lung compliance in an innovative way using—very—low frequency oscillometry.2 Another development in that period concerned the diagnostic toolbox, where collaborative efforts were again key. For instance, an inter-university collaboration later supported by Horizon 2020 (https://cordis.europa.eu/project/id/101016131) led to a time-efficient AI based detection of patterns of parenchymal and vascular abnormality on CT images from COVID patients (https://icovid.ai/). For the COVID patients admitted to our hospital the automated analysis showed a marked radiological improvement at 10 weeks after hospitalization and a slower improvement to almost normal at 6 months.3 Our hospital being located in the Brussels Capital Region, this also meant that there was a sizeable patient population of African descent, involving mostly people from Congo or Rwanda as a result of our country's history. This posed a very practical problem when attempting to establish presence of lung restriction, one of the functional hallmarks of COVID-affected lungs. Lung restriction is usually defined by a total lung capacity below its limits of normal, for which we used local (Caucasian) reference equations because the Global Lung function Initiative (GLI) did not include static lung volumes at the time. The original aim of GLI (https://www.ersnet.org/science-and-research/ongoing-clinical-research-collaborations/the-global-lung-function-initiative/) was to incorporate globally sourced data to establish reference equations for selected lung function variables. But with increased complexity of the measured lung function indices, data sets became increasingly limited in terms of ethnicity and geographical origin. The jury is still very much out on how to deal with the underrepresentation of various ethnicities in GLI. Another point of discussion elicited by trying to measure lung function in COVID times, concerned the interpretation of the diffusing capacity measure itself (for monoxide, DLco) and its dependence on (restricted) lung volume.4 Again, this has stimulated multi-centric work involving Belgian laboratories that redirects the focus towards lung efficiency measurements such as transfer coefficient Kco, rather than solely relying on lung capacity measurements such as DLco. Cycling back to where the previous letter from Belgium in this Journal left off, with a citizen science project mapping air pollution in Flanders (May 2018 campaign), Brussels-Capital Region has followed suit and conducted a similar participative measurement campaign (October 2021). This showed a clear-cut pattern of air pollution mimicking socio-economic status of Brussels neighbourhoods, but it also brought us an encouraging result: while air pollution had not suddenly come to a halt with COVID, some positive effects could be discerned as a result of wide-spread working from home and increased bicycle use. The author declares no conflicts of interest.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.009 |
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; both teacher heads agree on what is shown here.
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".