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Record W2184299691 · doi:10.1186/s40697-015-0083-4

Life as a New Investigator

2015· article· en· W2184299691 on OpenAlexaffabout
Darren Bridgewater

Bibliographic record

VenueCanadian Journal of Kidney Health and Disease · 2015
Typearticle
Languageen
FieldMedicine
TopicHealth and Medical Research Impacts
Canadian institutionsMcMaster UniversityMcMaster University Medical Centre
Fundersnot available
KeywordsMantraMedicineMedical educationPsychology

Abstract

fetched live from OpenAlex

Life as a new investigator I remember being an eager third year undergraduate student with frost-tipped hair, walking through the corridors of Children’s Hospital of Western Ontario with my supervisor Dr. Matsell, a pediatric nephrologist, to see one of his patients with congenital kidney disease. Dr. Matsell explained the patient’s kidneys had not developed properly, resulting in poor function and thus the need for dialysis. When I asked him why their kidneys failed to develop properly, he simply responded with a slight nod and frown that implied he did not know. At this moment, it clicked. I grasped the clinical impact of the kidney research I was conducting as a summer student. This defining moment has not only helped motivate me through my Ph.D. and post-doctoral fellowship but remains a touchstone for me as an independent investigator. When I first started my laboratory, I was fortunate to share lab space with more established researchers and quickly learned to adopt the mantra of “beg, borrow, and steal” with an abundance of apologies and I.O.Us. In retrospect, integrating my lab with more established laboratories was fundamental to successful start up. These other researchers and their students provided invaluable technical support in experimental protocols, reagents, equipment, and knowledge of the inner workings of the university’s research core facilities. Another crucial early practice was hiring the right research personnel who had the ability to work independently with a high degree of self motivation, as the majority of my time was dedicated to developing my teaching curriculums, teaching, and writing grants. The excitement of buying new equipment and reagents, hiring personal, maintaining transgenic mice, and performing experiments quickly turned to a sense of urgency as the invoices started rolling in and my startup funds were quickly dwindling. Having had no luck in winning the lottery and realizing I had no choice but to pay these invoices, I jumped head first into the anxietyprovoking, stomach-churning process of writing grants. After my first grant was riddled with red marker by internal review, it was clear I had to develop my grantwriting skills. I therefore learned that identifying the right mentors and colleagues who cared about my success and would provide constructive feedback, no matter how painful, was the key to writing effective grants. With a newly found writing plan, experienced internal reviewers, and a smidgen of confidence, I applied for seven grants in my first year, and was awarded none. However, the reviewers’ comments from the granting agencies suggested a level of interest and enthusiasm for my research especially those from the Kidney Foundation of Canada. Therefore, with some positive reviews in hand, a little hope, guidance from mentors, and dedicated research staff, we addressed all the reviewers’ concerns, and in my second grant cycle, I finally received my first grant. Then second. Then third. Then fourth—most for modest amounts of money and for short duration. But at last, I had achieved grant success instrumental for career progression, and most importantly, this allowed me to further my research to generate foundational knowledge toward a cure for congenital kidney disease. I am indebted to the Hamilton Health Sciences New Investigator program, Bickell Foundation, NSERC, and Kidney Foundation of Canada for their support for me as a new investigator. Sadly, the gratification of grant success is too short lived. With funding in hand and productive graduate students settling in, publishing became the next top priority. Again, the cycle of rejections and revisions reared its ugly head and led me to question my research abilities as a new investigator. However, my lab members’ persistence, combined with input from my colleagues, helped me realize this was part of the normal process for a new investigator in getting published. After two rejections and completing a “major revision,” I finally received a response from the editor. This was a milestone moment—I remember exactly where I was and what I was doing—vacillating whether or not to open Correspondence: Bridgew@mcmaster.ca Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Canada Education Program in Anatomy, Hamilton Centre for Kidney Research, Pathology and Molecular Medicine, McMaster University Medical Centre, 1200 Main St. West, Hamilton, Ontario L8N 3Z5, Canada

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaMetaresearch
Domain: Incentives · Genre: Commentary
About the Canadian research system: yes · About a Canadian topic: no
Not applicablemedium
gptno category
Domain: not available · Genre: Commentary
About the Canadian research system: no · About a Canadian topic: no
Not applicablelow
models splitAgreement compares identical category sets and study designs across arms.

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)0.044
metaresearch head score (Gemma)0.209
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Incentives · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.956
Threshold uncertainty score0.367

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0440.209
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.002
Science and technology studies0.0110.008
Scholarly communication0.0310.017
Open science0.0040.019
Research integrity0.0190.042
Insufficient payload (model declined to judge)0.1100.084

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 head0.181
GPT teacher head0.410
Teacher spread0.229 · 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

Labeled directly by 2 models reading the full record.

Metaresearch

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designNot applicable
DomainIncentives
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

Citations1
Published2015
Admission routes2
Has abstractyes

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