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Record W1598286635 · doi:10.1113/jphysiol.2013.268300

Rebuttal from Gaspard Montandon and Richard Horner

2014· letter· en· W1598286635 on OpenAlexaff
Gaspard Montandon, Richard L. Horner

Bibliographic record

VenueThe Journal of Physiology · 2014
Typeletter
Languageen
FieldNeuroscience
TopicNeuroscience of respiration and sleep
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsRespiratory systemOpioidAnesthesiaRespiratory ratePonsMedicineMicrodialysisBrainstemNeuroscienceReceptorPharmacologyInternal medicinePsychologyHeart rateCentral nervous system

Abstract

fetched live from OpenAlex

We agree with Lalley et al. (2014) that various brainstem sites may contribute to opioid-induced respiratory depression. Our focus here, however, is on respiratory rate depression by systemically administered drugs acting on μ-opioid receptors. Of all the potential neural sites where systemically administered μ-opioids could act, Lalley et al. suggest that the parabrachial/Kölliker–Fuse complex may be critically mediating respiratory rate depression. First, if pontine nuclei were responsible for rate suppression, then depression should not be observed in the absence of the pons. Still, respiratory slowing occurs in preparations where transections are performed caudal to the pons (Takita et al. 1997; Gray et al. 1999). Also, the blocking of μ-opioid receptors alone in pontine regions has a stimulatory effect on respiratory rate that can be misinterpreted as a reversal of opioid-induced respiratory depression (Phillips et al. 2012; Prkic et al. 2012). Using microdialysis tools to locally manipulate cells, we showed that the preBötC is highly sensitive to μ-opioid receptor agonists and mediates respiratory rate depression by systematically administered μ-opioids (Montandon et al. 2011). One caveat raised when using local drug application is that drug concentration in tissue is unknown as diffusion depends on the molecule, concentration and route of perfusion. To circumvent these issues, we designed strategies to assess how effective drug perfusion is. First, we simulated drug diffusion ex situ and found that after 2 h of perfusion less than 18% of the delivered concentration was present beside the probe membrane and 5% was found at a 1 mm distance (Grace et al. 2014), which invalidates the notion that drugs diffuse beyond the preBötC and affect other respiratory nuclei. Secondly, perfusion close to the preBötC was more potent in causing rate depression or its reversal than perfusion further away (Montandon et al. 2011). Also, if the μ-opioid receptor antagonist naloxone was affecting other nuclei, it should also block the impact of systemic μ-opioids on genioglossus muscle activity since the hypoglossal premotor/motor neurons are close to the preBötC. It did not, however, and we previously revealed separate medullary sites for hypoglossal motor suppression (Hajiha et al. 2009; Montandon et al. 2011). In conclusion, we dispute the belief that the preBötC plays an indirect role in opioid-induced respiratory rate depression. Other sites may indeed mediate other components of respiratory depression, such as reduced respiratory drive transmission and upper airway dysfunction, but based on the evidence discussed (Montandon et al. 2011), we restate that the preBötC plays a critical role in mediating opioid-induced respiratory rate depression. Readers are invited to give their views on this and the accompanying CrossTalk articles in this issue by submitting a brief comment. Comments may be posted up to 6 weeks after publication of the article, at which point the discussion will close and authors will be invited to submit a ‘final word’. To submit a comment, go to http://jp.physoc.org/letters/submit/jphysiol;592/6/1167 Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article. None declared.

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)0.001
metaresearch head score (Gemma)0.012
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: none
Teacher disagreement score0.130
Threshold uncertainty score0.436

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0020.002
Research integrity0.0050.007
Insufficient payload (model declined to judge)0.1300.117

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 head0.036
GPT teacher head0.261
Teacher spread0.225 · 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".

Quick stats

Citations7
Published2014
Admission routes1
Has abstractyes

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