Response to Alici
Bibliographic record
Abstract
In response to our study on the diagnostic challenges of distinguishing protracted from new-onset delirium,1 Alici raises two important questions for research on the persistence of delirium: use of the Confusion Assessment Method (CAM) for assessing the presence of persistent delirium and frequency of reassessments.2 As for the CAM, use of the question “Is there evidence of an acute change in mental status from baseline” is potentially problematic for two reasons. First, the question does not specify which baseline the interviewer is referring to, the original baseline before admission (weeks or months earlier) or a more-recent baseline since admission. Second, it does not distinguish new-onset delirium from a fluctuation in mental state in the context of persistent delirium. The original baseline was used as the reference;1 most informants told the interviewer that the patient's mental state had not been the same since admission to the hospital. Admittedly, this is an additional potential limitation of the study. There is a need to develop and validate a CAM modified to assess the persistence of delirium. As to the frequency of reassessments for persistent delirium, daily face-to-face reassessments may be feasible in hospital and postacute care settings,3, 4 but they are not feasible after discharge in most healthcare systems (including our system), where patients may be dispersed over a large metropolitan area and may move to different settings during the follow-up period. It was challenging to complete even the two face-to-face reassessments described in the present study. The limited number of reassessments was, nonetheless, an acknowledged limitation of the study. Future studies of the persistence of delirium conducted in special healthcare contexts (e.g., managed care programs) may be able to conduct more-frequent reassessments over longer follow-up periods. Conflict of Interest: The editor in chief has reviewed the conflict of interest checklist provided by the authors and has determined that the authors have no financial or any other kind of personal conflicts with this paper. This research was supported by Canadian Institutes of Health Research Operating Grant FRN-102523. Author Contributions: Cole: study concept, obtaining funding, study implementation, data collection, data analysis, writing of the paper. Bailey: study concept, obtaining funding, data collection, approval of final version of the paper. Bonnycastle: study concept, obtaining funding, data collection, data analysis, approval of final version of the paper. McCusker: obtaining funding, data analysis, approval of final version of the paper. Fung: data collection, data analysis, approval of final version of the paper. Ciampi, Belzile, Bai: data analysis, approval of final version of the paper. Sponsor's Role: The funding organization approved the design of the study but had no role in the conduct of the study; collection, management, analysis, or interpretation of the data; preparation, review, or approval of the manuscript; of decision to submit the manuscript for publication.
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 | 0.006 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.026 | 0.018 |
| Insufficient payload (model declined to judge) | 0.107 | 0.050 |
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".