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Medical Researcher Rediscovers Integration

Posted by timothy on from the it's-all-mathy dept.

parallel_prankster writes "I find this paper very amusing. From the abstract: 'To develop a mathematical model for the determination of total areas under curves from various metabolic studies.' Hint! If you replace phrases like 'curves from metabolic studies' with just 'curves,' then you'll note that Dr. Tai rediscovered the rectangle method of approximating an integral. (Actually, Dr. Tai rediscovered the trapezoidal rule.). Apparently this is called 'Tai's Model.'"

4 of 473 comments (clear)

  1. Re:So how is a 16 year old report news? by Anonymous Coward · · Score: 5, Informative

    Diabetes Care February 1994 vol. 17 no. 2 152-154

    That this study was stating the obvious was also noted 16 years ago. Unfortunately, often these follow up comments are very hard to find. Seeing all these comments, the article perhaps should have been pulled.

    Diabetes Care. 1994 Oct;17(10):1223-4; author reply 1225-6. Comments on Tai's mathematic model. Wolever TM. Comment on: * Diabetes Care. 1994 Feb;17(2):152-4. PMID: 7821151

    Diabetes Care. 1994 Oct;17(10):1224-5; author reply 1225-7. Tai's formula is the trapezoidal rule. Monaco JH, Anderson RL. Comment on: * Diabetes Care. 1994 Feb;17(2):152-4. PMID: 7677819

    Diabetes Care. 1994 Oct;17(10):1225. Modeling metabolic curves. Shannon AG, Owens DR. Comment on: * Diabetes Care. 1994 Feb;17(2):152-4. PMID: 7821152

    Diabetes Care. 1994 Oct;17(10):1223; author reply 1225-6. Determination of the area under a curve. Bender R. Comment on: * Diabetes Care. 1994 Feb;17(2):152-4. PMID: 7821150

  2. Damning Followup by FrootLoops · · Score: 5, Informative

    Tai's article was printed in February of 1994. An author comment printed in the October 1994 issue is titled "Tai's formula is the trapezoidal rule." I don't have full text access to either, but the title of the followup is not encouraging.

  3. Re:So how is a 16 year old report news? by Graff · · Score: 5, Informative

    There's a great ancient method for estimating curves that we used to use all the time in instrumental analysis.

    1. take a strip of paper that has a graph on it
    2. cut out two pieces
      1. the area under the curve that you want to measure
      2. a rectangle a certain amount of units high and wide
    3. weigh each piece of paper
    4. multiply the height and width (in the units you are measuring) of the rectangular piece
    5. divide that by the weight of the rectangular piece
    6. multiply that by the weight of the curve piece

    You now have the area under the curve!

    It's a lot quicker and easier than most other methods for estimating the area if you are dealing with a complex curve. Of course now that computers are used to gather the data instead of strip charts it's even easier for the computer to just add up the magnitude of all the data points and multiply by some constant to get a decent estimate.

    --
    Sapere aude!
  4. Re:Number of citations... by bothemeson · · Score: 5, Informative
    in a word, yes, check out almost any medical stats methodology - it looks sort of right if you have only degree level maths but, eg, statisticians have pretty much given up on pointing out that treating binned averages of a population as raw data typically invalidates the method under consideration, rendering the results speculative at best.

    researchers will tend to insist that what they have handed over is raw data because they have (or a research associate, or Excel! has) only performed a few simple transformations on it and, that being many months ago, probably have forgotten the fact. one can either keep performing extra (unpaid and unasked for) analyses showing that this distribution verges on the impossible (and risk not be asked for help in future) or shut up and get cited and allow your reputation to grow

    having said that, the same is true for many scientific practitioners and, indeed, the majority of published journal papers - the peer review generally doesn't extend to a competent mathematical practitioner (still less frequently a statistician) and most academics do not appear to consider that anything beyond their (often high school- or graduate-level) understanding of mathematics is required, after all (like the paper concerned here) building on previously published and highly cited work of little worth is all that's required for a career