Bibliographic record
Abstract
Letters19 September 2006Preventing Scientific FraudDominique R. Garrel, MD, Rémi Rabasa-Lhoret, MD, and Pierre Boyle, PhD, MHADominique R. Garrel, MDFrom the University of Montréal, Montréal, Québec, Canada.Search for more papers by this author, Rémi Rabasa-Lhoret, MDFrom the University of Montréal, Montréal, Québec, Canada.Search for more papers by this author, and Pierre Boyle, PhD, MHAFrom the University of Montréal, Montréal, Québec, Canada.Search for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/0003-4819-145-6-200609190-00019 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail TO THE EDITOR: We read the article on research misconduct and the Poehlman case (1) with great interest. Eric Poehlman was hired by the University of Montréal and obtained a senior Canadian Chair position from the Canadian Institutes of Health Research in 2002. He held a professorship position in the department of nutrition at our university and was dismissed when we learned of his misconduct at the University of Vermont. The news of Dr. Poehlman's scientific fraud was a devastating blow to all Canadian scientists and students who worked with him. The possibility that scientific misconduct by Dr. Poehlman had ...References1. Sox HC, Rennie D. Research misconduct, retraction, and cleansing the medical literature: lessons from the Poehlman case. Ann Intern Med. 2006;144:609-13. [PMID: 16522625] LinkGoogle Scholar2. Karelis AD, Henry JF, Malita F, St-Pierre DH, Vigneault I, Poehlman ET, et al. Comparison of insulin sensitivity values using the hyperinsulinemic euglycemic clamp: 2 vs 3 hours [Letter]. Diabetes Metab. 2004;30:413-4. [PMID: 15671908] CrossrefMedlineGoogle Scholar3. St-Pierre DH, Karelis AD, Cianflone K, Conus F, Mignault D, Rabasa-Lhoret R, et al. Relationship between ghrelin and energy expenditure in healthy young women. J Clin Endocrinol Metab. 2004;89:5993-7. [PMID: 15579749] CrossrefMedlineGoogle Scholar4. Karelis AD, Brochu M, Rabasa-Lhoret R, Garrel D, Poehlman ET. Clinical markers for the identification of metabolically healthy but obese individuals [Letter]. Diabetes Obes Metab. 2004;6:456-7. [PMID: 15479222] CrossrefMedlineGoogle Scholar5. Conus F, Allison DB, Rabasa-Lhoret R, St-Onge M, St-Pierre DH, Tremblay-Lebeau A, et al. Metabolic and behavioral characteristics of metabolically obese but normal-weight women. J Clin Endocrinol Metab. 2004;89:5013-20. [PMID: 15472199] CrossrefMedlineGoogle Scholar6. Karelis AD, St-Pierre DH, Conus F, Rabasa-Lhoret R, Poehlman ET. Metabolic and body composition factors in subgroups of obesity: what do we know? J Clin Endocrinol Metab. 2004;89:2569-75. [PMID: 15181025] CrossrefMedlineGoogle Scholar7. St-Pierre DH, George V, Rabasa-Lhoret R, Poehlman ET. Genetic variation and statistical considerations in relation to overfeeding and underfeeding in humans. Nutrition. 2004;20:145-54. [PMID: 14698030] CrossrefMedlineGoogle Scholar Author, Article, and Disclosure InformationAffiliations: From the University of Montréal, Montréal, Québec, Canada.Disclosures: None disclosed. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetailsSee AlsoResearch Misconduct, Retraction, and Cleansing the Medical Literature: Lessons from the Poehlman Case Harold C. Sox and Drummond Rennie Preventing Scientific Fraud Harold C. Sox and Drummond Rennie Preventing Scientific Fraud David W. Noble Preventing Scientific Fraud Eugene Garfield , Marie McVeigh , and Marion Muff Metrics 19 September 2006Volume 145, Issue 6Page: 473KeywordsConflicts of interestElectrode recordingNutritionResearch designResearch fundingScientific misconductScientists ePublished: 19 September 2006 Issue Published: 19 September 2006 Copyright & PermissionsCopyright © 2006 by American College of Physicians. All Rights Reserved.PDF downloadLoading ...
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.055 | 0.370 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.023 | 0.013 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.032 | 0.027 |
| Insufficient payload (model declined to judge) | 0.077 | 0.048 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".