Tranexamic acid in traumatic brain injury: an explanatory study nested within the CRASH-3 trial.

PURPOSE: The CRASH-3 trial is a randomised trial of tranexamic acid (TXA) on death and disability in patients with traumatic brain injury (TBI). It is based on the hypothesis that early TXA treatment can prevent deaths from post-traumatic intracranial bleeding. The results showed that timely TXA treatment reduces head injury deaths in patients with reactive pupils and those with a mild to moderate GCS at baseline. We examined routinely collected CT scans in a sample of 1767 CRASH-3 trial patients to explore if, why, and how patients are affected by TXA. METHODS: The CRASH-3 IBMS is an explanatory study nested within the CRASH-3 trial. We measured the volume of intracranial bleeding on CT scans using established methods (e.g. ABC/2). RESULTS: Patients with any un-reactive pupil had a median intracranial bleeding volume of 60 ml (IQR 18-101 ml) and patients with reactive pupils had a median volume of 26 ml (IQR 1-55 ml). Patients with severe GCS had median intracranial bleeding volume of 37 ml (IQR 3-75 ml) and patients with moderate to mild GCS had a median volume of 26 ml (IQR 0.4-50 ml). For every hour increase from injury to the baseline scan, the risk of new bleeding on a further scan decreased by 12% (adjusted RR = 0.88 [95% CI 0.80-0.96], p = 0.0047). CONCLUSION: Patients with reactive pupils and/or mild to moderate GCS may have benefited from TXA in the CRASH-3 trial because they had less intracranial bleeding at baseline. However, because bleeding occurs soon after injury, treatment delay reduces the benefit of TXA

Causation at different levels : Tracking the commitments of mechanistic explanations

This paper tracks the commitments of mechanistic explanations focusing on the relation between activities at different levels. It is pointed out that the mechanistic approach is inherently committed to identifying causal connections at higher levels with causal connections at lower levels. For the mechanistic approach to succeed a mechanism as a whole must do the very same thing what its parts organised in a particular way do. The mechanistic approach must also utilise bridge principles connecting different causal terms of different theoretical vocabularies in order to make the identities of causal connections transparent. These general commitments get confronted with two claims made by certain proponents of the mechanistic approach: William Bechtel often argues that within the mechanistic framework it is possible to balance between reducing higher levels and maintaining their autonomy at the same time, whereas, in a recent paper, Craver and Bechtel argue that the mechanistic approach is able to make downward causation intelligible. The paper concludes that the mechanistic approach imbued with identity statements is no better candidate for anchoring higher levels to lower ones while maintaining their autonomy at the same time than standard reductive accounts are, and that what mechanistic explanations are able to do at best is showing that downward causation does not exist

Mechanistic artefact explanation

One thing about technical artefacts that needs to be explained is how their physical make-up, or structure, enables them to fulfil the behaviour associated with their function, or, more colloquially, how they work. In this paper I develop an account of such explanations based on the familiar notion of mechanistic explanation. To accomplish this, I outline two explanatory strategies that provide two different types of insight into an artefact’s functioning, and show how human action inevitably plays a role in artefact explanation. I then use my own account to criticize other recent work on mechanistic explanation and conclude with some general implications for the philosophy of explanation.Keywords: Artefact; Technical function; Explanation; Levels of explanation; Mechanisms

Mechanistic Behavior of Single-Pass Instruction Sequences

Earlier work on program and thread algebra detailed the functional, observable behavior of programs under execution. In this article we add the modeling of unobservable, mechanistic processing, in particular processing due to jump instructions. We model mechanistic processing preceding some further behavior as a delay of that behavior; we borrow a unary delay operator from discrete time process algebra. We define a mechanistic improvement ordering on threads and observe that some threads do not have an optimal implementation.Comment: 12 page

Making Sense of Interlevel Causation in Mechanisms from a Metaphysical Perspective

According to the new mechanistic approach, an acting entity is at a lower mechanistic level than another acting entity if and only if the former is a component in the mechanism for the latter. Craver and Bechtel :547–563, 2007. doi:10.1007/s10539-006-9028-8) argue that a consequence of this view is that there cannot be causal interactions between acting entities at different mechanistic levels. Their main reason seems to be what I will call the Metaphysical Argument: things at different levels of a mechanism are related as part and whole; wholes and their parts cannot be related as cause and effect; hence, interlevel causation in mechanisms is impossible. I will analyze this argument in more detail and show under which conditions it is valid. This analysis will reveal that interlevel causation in mechanisms is indeed possible, if we take seriously the idea that the relata of the mechanistic level relation are acting entities and accept a slightly modified notion of a mechanistic level that is highly plausible in the light of the first clarification

Merleau-Ponty’s implicit critique of the new mechanists

I argue (1) that what (ontic) New Mechanistic philosophers of science call mechanisms would be material Gestalten, and (2) that Merleau-Ponty’s engagement with Gestalt theory can help us frame a standing challenge against ontic conceptions of mechanisms. In short, until the (ontic) New Mechanist can provide us with a plausible account of the organization of mechanisms as an objective feature of mind-independent ontic structures in the world which we might discover – and no ontic Mechanist has done so – it is more conservative to claim that mechanistic organization is instead a mind-dependent aspect of our epistemic strategies of mechanistic explanation

A unified mechanistic model of niche, neutrality and violation of the competitive exclusion principle

The origin of species richness is one of the most widely discussed questions in ecology. The absence of unified mechanistic model of competition makes difficult our deep understanding of this subject. Here we show such a two-species competition model that unifies (i) a mechanistic niche model, (ii) a mechanistic neutral (null) model and (iii) a mechanistic violation of the competitive exclusion principle. Our model is an individual-based cellular automaton. We demonstrate how two trophically identical and aggressively propagating species can stably coexist in one stable homogeneous habitat without any trade-offs in spite of their 10% difference in fitness. Competitive exclusion occurs if the fitness difference is significant (approximately more than 30%). If the species have one and the same fitness they stably coexist and have similar numbers. We conclude that this model shows diffusion-like and half-soliton-like mechanisms of interactions of colliding population waves. The revealed mechanisms eliminate the existing contradictions between ideas of niche, neutrality and cases of violation of the competitive exclusion principle

Combining mechanistic and data-driven techniques for predictive modelling of wastewater treatment plants

Mechanistic models are widely used for modelling of wastewater treatment plants. However, as they are based on simplified and incomplete domain knowledge, they often lack accurate predictive capabilities. In contrast, data-driven models are able to make accurate predictions, but only in the operational regions that are sufficiently described by the dataset used. We investigate an alternative hybrid model, combining mechanistic and data-driven techniques. We show that the hybrid approach combines the strengths of both modelling paradigms. It allows for accurate predictions out of the training dataset without the need for complete domain knowledge. Moreover, this approach is not limited to wastewater treatment plants and can potentially be applied wherever mechanistic models are used