Sequence of mixed ophthalmic operating lists: A national survey of UK consultants

Ophthalmic surgical operating lists include intraocular and extraocular procedures, as well as clean non-infectious and dirty infectious cases. Patient age, diabetic status, local or general anaesthesia must be considered during ophthalmic theatre scheduling. Traditionally children and ‘clean cases’ are prioritised. However, factors such as the need for an interpreter, patient transport and latex allergy affect the sequencing of ophthalmic lists. An electronic survey was sent to all UK ophthalmology consultants through the Royal College of Ophthalmologists registry, enquiring about their preference in sequencing mixed theatre lists, what operations they considered clean and dirty, and the presence of departmental protocol for list sequencing. There was a 16.9% response rate ( n = 222/1311). A majority of 75.2% ( n = 167/222) had mixed operating lists of intraocular and extraocular cases. Of those performing mixed operating lists, 44.3% ( n = 74/167) stated they would operate on intraocular cases before extraocular cases, and 92.8% ( n = 155/167) would perform ‘clean’ before ‘dirty’ cases. Fifty-nine per cent ( n = 98/167) have a departmental protocol to help determine list order. This survey has demonstrated that there is a trend to perform ‘clean’ before ‘dirty’ and intraocular before extraocular cases. Given the results of the survey, we outline our recommendation on how to sequence mixed ophthalmic theatre lists.

Erratum: Hopp, A.K., et al. Regulation of Glucose Metabolism by NAD+ and ADP-Ribosylation. Cells 2019, 8, 890

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Reward associations do not explain transitive inference performance in monkeys

Most accounts of behavior in nonhuman animals assume that they make choices to maximize expected reward value. However, model-free reinforcement learning based on reward associations cannot account for choice behavior in transitive inference paradigms. We manipulated the amount of reward associated with each item of an ordered list, so that maximizing expected reward value was always in conflict with decision rules based on the implicit list order. Under such a schedule, model-free reinforcement algorithms cannot achieve high levels of accuracy, even after extensive training. Monkeys nevertheless learned to make correct rule-based choices. These results show that monkeys’ performance in transitive inference paradigms is not driven solely by expected reward and that appropriate inferences are made despite discordant reward incentives. We show that their choices can be explained by an abstract, model-based representation of list order, and we provide a method for inferring the contents of such representations from observed data.

Optimal list order under partial memory constraints

Suppose that we are given a set of n elements which are to be arranged in some order. At each unit of time a request is made to retrieve one of these elements — the ith being requested with probability Pi. We show that the rule which always moves the requested element one closer to the front of the line minimizes the average position of the element requested among a wide class of rules for all probability vectors of the form P1 = p, P2= · ·· = Pn = (1 – p)/(n − 1). We also consider the above problem when the decision-maker is allowed to utilize such rules as ‘only make a change if the same element has been requested k times in a row', and show that as k approaches infinity we can do as well as if we knew the values of the Pi.

Optimal list order under partial memory constraints

Suppose that we are given a set of n elements which are to be arranged in some order. At each unit of time a request is made to retrieve one of these elements — the ith being requested with probability Pi. We show that the rule which always moves the requested element one closer to the front of the line minimizes the average position of the element requested among a wide class of rules for all probability vectors of the form P 1 = p, P 2= · ·· = Pn = (1 – p)/(n − 1). We also consider the above problem when the decision-maker is allowed to utilize such rules as ‘only make a change if the same element has been requested k times in a row', and show that as k approaches infinity we can do as well as if we knew the values of the Pi.