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Record W4403989797 · doi:10.3389/fphys.2024.1504497

Commentary: Effects of occlusion pressure on hemodynamic responses recorded by near-infrared spectroscopy across two visits

2024· letter· en· W4403989797 on OpenAlexaff
Nicholas Rolnick, J McEwen, Victor De Queiros

Bibliographic record

VenueFrontiers in Physiology · 2024
Typeletter
Languageen
FieldMedicine
TopicOptical Imaging and Spectroscopy Techniques
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsHemodynamicsOcclusionMedicineBlood pressureCardiologyInternal medicine

Abstract

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Ischemic preconditioning (IPC) involves brief cycles of ischemia and reperfusion that can be applied prior to physical testing to either enhance physical performance or reduce exerciseinduced muscle damage (Franz et al., 2018;Salvador et al., 2016). Ischemia is typically induced using inflatable cuffs placed on the proximal regions of the upper or lower limbs. Many studies use arbitrary pressures (e.g., 220 mmHg) (Salvador et al., 2016); however, this method poses challenges since the same absolute pressure can lead to varying levels of tissue pressure. Factors like limb circumference and cuff size can significantly influence the pressure required to achieve arterial occlusion (de Queiros et al., 2024). As a result, some researchers, such as Desanlis et al. (2024), have focused on refining the standardization of cuff pressures in IPC interventions (Desanlis et al., 2024). Their study aimed to explore how different occlusion pressures affect hemodynamic responses, contributing to more accurate pressure prescription during IPC. While we commend the authors for their valuable efforts, there are some important considerations to keep in mind when interpreting the findings.The study evaluated peripheral hemodynamic responses in 35 young male participants using a between-subjects design, where participants underwent partial and complete blood-flow occlusion, both absolute (50 mmHg [G1] and 250 mmHg [G3]) and individualized (systolic blood pressure + 50 mmHg, G2), in the left arm under resting conditions (Desanlis et al., 2024). The protocol applied 3 intervals of 7 minutes of pressure, separated by 10-20 minutes of rest, while assessing tissue oxygenation (TSI), oxyhemoglobin (O2Hb), and deoxyhemoglobin (HHb) using nearinfrared spectroscopy (NIRS). Their findings demonstrated greater deoxygenation and faster reoxygenation in participants subjected to occlusion pressures exceeding systolic blood pressure (G2 and G3) compared to partial occlusion (G1), with no significant differences between the G2 and G3 groups. The authors concluded that individualizing pressure provides the optimal response to IPC and that 250 mmHg may be excessive.The most significant issue we identified is that the between-subjects methodology and the lack of true personalization of applied pressures limit the ability to draw firm conclusions regarding the impact of IPC pressure on tissue oxygenation responses. Since the authors employed a betweensubjects design, participants were randomized to one of three conditions rather than undergoing each experimental condition.Although the authors aimed to personalize the applied pressure by adjusting it relative to systolic blood pressure, the absence of detailed reporting-such as each participant's arm circumference and the cuff width used to determine systolic blood pressure-raises concerns about whether the pressures applied were truly individualized. Systolic blood pressure can only be equated to limb occlusion pressure when the cuff width and bladder type match those used to measure systolic blood pressure (Rolnick et al., 2021(Rolnick et al., , 2023)). For example, a narrower cuff would lead to higherthan-expected pressures, whereas a wider cuff would require less pressure to achieve occlusion (Graham et al., 1993).Moreover, since the limb circumferences of the participants were not reported, we question whether the individualized pressure (G2) was accurately applied across subjects. The same pressure increase (e.g., 50 mmHg) could produce varying physiological effects depending on limb size (Jessee et al., 2016). This issue is especially relevant in a between-subjects design compared to a within-subjects design, where each participant would experience all conditions and act as their own control. Although arm circumference would still be useful to report in a within-subjects design, its importance diminishes as each participant's response can be directly compared across conditions.Last, it is important to note that the authors did not address the limitations of true personalization in their limitations section. Specifically, the inability to fully personalize pressure without reporting key device-related characteristics, such as cuff width and bladder design, means that their "individualized" pressure prescription was only likely partially reflective of the impact of the different pressure schemes. This oversight further reduces confidence in the study's conclusions regarding the optimal approach to IPC pressure prescription.In studies such as this, where the primary aim is to determine the effect of applied pressure on tissue oxygenation responses, a within-subjects design would better address individual differences in limb size, thereby enhancing the precision of NIRS data and the overall validity of the findings.Given the influence of cuff characteristics on tissue pressure, it is crucial to report the specific characteristics of the device used in studies involving blood flow restriction or IPC training. This improves the interpretation of results, particularly when the independent variable of interest is pressure. In the study by Desanlis et al. (2024), it would have been beneficial to include participants' arm circumference measurements, considering that the use of arbitrary pressures could result in variable responses due to differences in limb circumference.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.098
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.048
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.098
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0020.003
Open science0.0050.002
Research integrity0.0480.026
Insufficient payload (model declined to judge)0.0190.015

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.

Opus teacher head0.005
GPT teacher head0.300
Teacher spread0.296 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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".

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Citations1
Published2024
Admission routes1
Has abstractyes

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