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Record W2083400358 · doi:10.1373/clinchem.2015.239129

A Day in the Life of Dr. Bean and How the NIH Is Wasting $20 Billion per Year

2015· article· en· W2083400358 on OpenAlexaff
Eleftherios P. Diamandis

Bibliographic record

VenueClinical Chemistry · 2015
Typearticle
Languageen
FieldMedicine
TopicHealth and Medical Research Impacts
Canadian institutionsMount Sinai Hospital
Fundersnot available
KeywordsWastingGerontologyMedicineDemographyEnvironmental healthInternal medicineSociology

Abstract

fetched live from OpenAlex

Interviewer: Good morning, Dr. Bean. Thank you for accepting this opportunity to be interviewed. Your comments will be very useful for the new generation of young and upcoming scientists. Given your very successful career, you likely have much to say and lots of advice to give. My objective this morning is to describe one of your typical days. I am sure it will be fun. Should we start? Dr. Bean: Yes, my pleasure. Please go ahead. Interviewer: My interview will be broken into blocks of 2 hours. So, let us start with the first 2 hours of your day. Dr. Bean: Sure, I get up at 6 AM. I first make coffee and eat my breakfast, which brings me to 6:30 AM; then I do my exercise on a treadmill I have at home, finishing at 7:30 AM. You see, at 62, I must do this, otherwise, who knows what might happen. Then, I walk to work and open my office at exactly 8 AM. Interviewer: Sounds great. I guess at this time you are well-rested, relaxed, and ready to go. Dr. Bean: Absolutely! This is the premium time of my day from 8 to 10 AM. And I have strict instructions to my secretary—never book any appointments in this time block. I want this time for myself, for the most challenging part of my job. Interviewer: And what is that? Dr. Bean: Writing grants. You see, now I am working on a rather large NIH grant that is worth $2 million over 5 years. If I get it, it will guarantee about 40% of the lab's budget for a long time. Interviewer: I see; but since this seems to be a rather important issue, do you mind if we go through the rest of the day and then come back to the grants? Dr. Bean: This is reasonable. Let me now cover the 10–12 AM block. This is devoted to various meetings; with my students, my manager, company representatives, etc. Interviewer: Which brings us quickly to noon? Dr. Bean: Yes. At that time I try to catch-up with my e-mail. By noon, I would have at least 50 emails, and a few would require immediate attention. By the time I clear them it is lunch time. And by the time I finish lunch, it is 1:30 PM already. This is the time of the day to make a strong coffee and enjoy it in my office. Interviewer: And the 2 PM block is now ready? Dr. Bean: Yes. This block is reserved for phone calls and more meetings with my students and post-docs. Sometimes we have committee meetings, or deal with University affairs such as teaching etc. On other occasions, I work on my latest manuscripts. Interviewer: And what about the 4–6 PM slot? Dr. Bean: Blocked again; religiously; to continue my work on grants. But we will come back to that in a moment. At 6 PM I have another look at the e-mail just in case there is a burning issue, and by 6:30 PM I am ready to walk back home, dinner at 7 PM, digestion by 8 PM, and work on cleaning out all of my e-mails between 8 and 10 PM. After that, see you tomorrow. Interviewer: But don't you read anything, Dr. Bean? Dr. Bean: Of course I do! Nature and Science, cover to cover, but I keep this activity for when I travel. When I am at airports and on planes, I read, read, read. Interviewer: Ok; but let us go back to grants for now. You seem to be spending about half of your best hours of the day with grants. Do you do this in certain months of the year? Dr. Bean: Yes, 12 months per year. There is no such a thing as I finished a grant; you finish one and you start a new one; it is a continuous, nonstop exercise. Interviewer: Wow! I did not know that. How many grants are you submitting per year? Dr. Bean: About 20. Interviewer: This sounds totally insane—do you need so much money? What is your success rate? Dr. Bean: Same as everybody else's. About a 5% success rate; we send in 20 and we would be lucky if we get one; and I need at least 3 active grants to cover my lab's expenses. Interviewer: And what do you do with the grants that are not funded? Dr. Bean: Same as everybody else. We throw them in a blue plastic bin, otherwise known as the recycle bin. Interviewer: That's a shame, Dr. Bean. You just said that you spend your best time doing this and you now say that your best work ends up in the garbage? Dr. Bean: Yes, this is what I say. Interviewer: How much are you making per hour? Dr. Bean: About $100, and for 4 hours per day doing this, it is worth $400 per day, or roughly, $100,000 per annum. Interviewer: Going to garbage? Dr. Bean: Well, sort of. Interviewer: And how many Dr. Beans are doing this in the USA alone? Dr. Bean: Well, I do not know exactly but Google says that about 100 000 grant applications are submitted in the USA alone every year, so there must be at least 100 000 applicants like me! Interviewer: And if I multiply 100 000 by 100 000 do I get 10 billion dollars in wasted wages every year? Dr. Bean: Damn right! And you have not calculated the time wasted for panel members to review the grants, the administrative costs, etc.; the likely wasted wages may well be 20 billion instead or about 70% of the whole NIH budget! Interviewer: You seem to be very expensive and very wasteful, Dr. Bean; do the NIH officials know that? Dr. Bean: I do not know, ask them! Interviewer: And what do you advise the young scientists? Dr. Bean: I do not know—perhaps not printing their grants? Dispose of them electronically? But … wait a second here; I see that a new e-mail from NIH just arrived, and it reads: “we are pleased to inform you …” WHOA! My latest grant has been approved! I am sorry but I have to leave immediately to go and buy the champagne for the celebration with my lab and then leave for a 1-week vacation. I fully deserve it. I made it to the 5% of lucky applicants. See you in 1 week. I am coming back fresh to work on my next grant application.

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)0.008
metaresearch head score (Gemma)0.035
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.992
Threshold uncertainty score0.144

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.035
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0090.003
Scholarly communication0.0070.006
Open science0.0010.003
Research integrity0.0050.013
Insufficient payload (model declined to judge)0.0430.017

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 head0.393
GPT teacher head0.502
Teacher spread0.109 · 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.

Study designNot applicable
DomainEvaluation
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

Citations0
Published2015
Admission routes1
Has abstractyes

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