Does normalization of prolactin levels result in weight loss in patients with prolactin secreting pituitary adenomas?
Bibliographic record
Abstract
Clinical EndocrinologyVolume 56, Issue 4 p. 562-562 Does normalization of prolactin levels result in weight loss in patients with prolactin secreting pituitary adenomas? R. Yermus, Corresponding Author R. Yermus Mount Sinai Hospital, Toronto, Ontario, CanadaR. Yermus, 25 Evita Ct, Thornhill, Ontario, L4J 8K6, CanadaSearch for more papers by this authorS. Ezzat, S. Ezzat Mount Sinai Hospital, Toronto, Ontario, CanadaSearch for more papers by this author R. Yermus, Corresponding Author R. Yermus Mount Sinai Hospital, Toronto, Ontario, CanadaR. Yermus, 25 Evita Ct, Thornhill, Ontario, L4J 8K6, CanadaSearch for more papers by this authorS. Ezzat, S. Ezzat Mount Sinai Hospital, Toronto, Ontario, CanadaSearch for more papers by this author First published: 14 May 2002 https://doi.org/10.1046/j.1365-2265.2002.01510.xCitations: 8Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article.Citing Literature Volume56, Issue4April 2002Pages 562-562 RelatedInformation
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".