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Record W2937343473 · doi:10.1093/ajcp/aqz037

H-index and Academic Medicine

2019· letter· en· W2937343473 on OpenAlexaff
Ishwarlal Jialal, William E. Schreiber, Dean Giustini

Bibliographic record

VenueAmerican Journal of Clinical Pathology · 2019
Typeletter
Languageen
FieldMedicine
TopicHealth and Medical Research Impacts
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsIndex (typography)MedicineStatisticsMathematicsComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

I read with great interest the recent minireview by Schreiber and Giustini1 on the h-index. They need to be lauded for bringing this important issue concerning academic medicine to the forefront of this journal, which is read by many pathologists. However, the issue of the h-index is most germane to academic pathologists. As a senior semiretired professor who attained the rank of Distinguished Professor, I was always surprised at how few faculty were aware of the h-index and its value to those involved in promotion and tenure. With a career spanning 42 years in academic medicine and called on frequently to be a reviewer for promotions and tenure both nationally and globally, I continue to find the h-index to be a valuable adjunctive tool. As the authors point out, it is an objective metric that is quantitative and cumulative. It rewards durability and sustained productivity. However, they also criticize the h-index. It is true that highly cited papers do not carry more weight. My retort is that in addition to the h-index, one has to use other metrics such as the number of citations of the top 10 publications etc, because this also helps resolve the contribution to multiauthor papers to a major extent. In this competitive era of limited funding, team science is a solution and single-author papers are extremely rare in biomedical research. Self-citation is an issue and is more so with Google Scholar, which counts all citations including self-citations, as compared to Web of Science, which, to the best of my knowledge, excludes self-citation. This is exemplified by the inflated Google scholar h-index compared to Scopus (Mendeley) and Web of Science. In an interesting paper, Engqvist and Frommen2 state that tripling self-citations increase the h-index by only 1. There is clearly a difference in the h-index from natural sciences to biomedical sciences. It is interesting that a physicist proposed it as a metric of scientific achievement. The h-index is cumulative and cannot provide an indication of recent productivity. However, this is always available on PubMed and on the curriculum vitae of the candidate. Schreiber and Giustini1 chose to highlight the variance in h-index using Nobel laureates, although members of the National Academy of Sciences might be more relevant with a much larger sample size. In the American Journal of Clinical Pathology in 2010 the h-index was evaluated in the first sextile of medical colleges in the United States. Interestingly, in this report, chairs of pathology had the best mean score of 51.2 with a range of 28 to 83.3 This might be a prudent exercise for our fraternity to revisit in 2020 to see how present-day chairs fair. In conclusion, the h-index is one metric that is objective and judges the cumulative contribution of a scholar. It should be used in conjunction with the top publications of the author/candidate and other sources of information. In this regard, it is important to bear in mind that bias, academic envy, and other prejudices can be manifest in subjective references. The h-index has attracted a lot of attention since it was first proposed in 2005. Only a few papers that even mention the h-index have appeared in the pathology and laboratory medicine literature. Because it is likely to be used as a measuring stick for academic achievement, we thought that a review for this audience was timely. Our criticisms of the h-index have been presented by other authors as well.1,2 They are included to ensure a balanced perspective. Reducing one’s reputation to a number is overly simplistic—not every great scientist leaves a long trail of papers in his or her wake. For all of its elegance (so typical of the physicist’s mind), the h-index is only as good as the original data, which come from an individual’s publication record and the associated citations. A careful examination of authorship for each publication, assigning credit where it is due, may reveal more about a scientist’s lifetime contributions than the index itself. We thank Dr Jialal for noticing our paper and providing his comments.

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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)0.005
metaresearch head score (Gemma)0.030
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Bibliometrics
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 score0.998
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.030
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0140.006
Scholarly communication0.0090.005
Open science0.0020.004
Research integrity0.1090.049
Insufficient payload (model declined to judge)0.0200.010

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 head0.282
GPT teacher head0.568
Teacher spread0.286 · 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

Citations7
Published2019
Admission routes1
Has abstractno

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