Erratum: asthma in the elderly: what we know and what we have yet to know
Bibliographic record
Abstract
This article was originally published online on 18 June 2014 Following the publication of our article [1.Yáñez A. Cho S.H. Soriano J.B. Rosenwasser L.J. Rodrigo G.J. Rabe K.F. Peters S. Niimi A. Asthma in the elderly: what we know and what we have yet to know.World Allergy Org J. 2014; 7: 8Abstract Full Text Full Text PDF PubMed Scopus (95) Google Scholar], we noticed that Dr Flavia C. L. Hoyte had inadvertently been omitted from the author list. FCLH declares she has no conflicts of interest. The author list has now been corrected and the amended authors contributions section included below. AY initiated and led the development of the paper as primary author, contributing to all of the sections and unifying the document. SCH and STH were co-project leaders. SHC wrote the Introduction. JC and CEB wrote on the impact of asthma. LPB and GWC wrote on management of asthma. RK, FCLH, PB, LF, AK, KR, and LR wrote on the aging lung. DKL, SHC, SP, and GJR wrote on diagnosis. JBS wrote on life expectancy. All authors reviewed and approved the final document. Asthma in the elderly: what we know and what we have yet to knowWorld Allergy Organization JournalVol. 7PreviewIn the past, asthma was considered mainly as a childhood disease. However, asthma is an important cause of morbidity and mortality in the elderly nowadays. In addition, the burden of asthma is more significant in the elderly than in their younger counterparts, particularly with regard to mortality, hospitalization, medical costs or health-related quality of life. Nevertheless, asthma in the elderly is still been underdiagnosed and undertreated. Therefore, it is an imperative task to recognize our current challenges and to set future directions. Full-Text PDF Open Access
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 | 0.007 | 0.073 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.030 | 0.023 |
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".