Vascular calcifications in an incident population of peritoneal dialysis patients
Bibliographic record
Abstract
Not more than 300 words. Do not use abbreviations. In the case of Portuguese language authors the abstract must also be translated into Portuguese. Key-Words: not more than 6, in alphabetical order, and the terms used (when possible) should be from the Medical Subject Headings list of the Index Medicus. In the case of Portuguese language authors the key-words must also be translated into Portuguese. Text: The order of the text should be as follows: Introduction, Subjects and Methods (any statistical method must be detailed in this section), Results, Discussion, Acknowledgments, References (see below), Tables, Captions and Figures. All pages should be numbered consecutively starting with the title page. Tables: References to tables should be made in order of appearance in the text and should be in Roman numerals in brackets, e.g. (Table II). Each table should be typed on a separate sheet and have a brief heading describing its contents. Figures: References to figures should be made in order of appearance in the text and should be in Arabic numerals in parentheses, e.g. (Fig. 2). If a figure has been published before, the original source must be acknowledged and written permission from the copyright holder must be submitted with the material. Patients shown in photographs should have their identity obscured or the picture must be accompanied by written permission to use the photograph. References: All the references, including those with only electronic sources, should be cited according to the “Vancouver Citation Style” which can be consulted on the Internet at: http://library.vcc.ca/downloads/VCC_VancouverStyleGuide.pdf. References must be numbered consecutively in the order in which they are cited in the text. Each reference should give the name and initials of all authors unless they are more than six, when only the first three should be given followed by et al. Authors’ names should be followed by the title of the article, journal abbreviations according to the style used in Index Medicus, the year of publication, the volume number and the first and last page numbers. References to books should contain the title of the book, followed by place of publi cation, publisher, year, and relevant pages.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| 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".