Between the first and second wave of the 2019 coronavirus pandemic (COVID-19): Presentation and crowding of attenders for mentale disorder and intossication/substance abuse
Bibliographic record
Abstract
Introduction During the 1st wave of CoViD-19 pandemic there was a drastic reduction in total number of accesses, with more serious cases and a exorbitant increase in crowding, due to access block. Objectives evaluate population who went to ED for (1) mental disorders requesting a psychiatric visit and for (2) intossication and substance abuse, between the first and second wave of the coronavirus pandemic Methods We enrolled all patients who went at our ED from May 1 to October 20, 2020 and during the same period of 2019. We analized: vital parameters, age, sex, exit severity codes, hospitalization rate, Crowding input factors (number of access, waiting time, priority time to doc), Crowding throughput factors (LOS: Length Of ED Stay), Crowding output factors (percentage of access block; Total Access Block Time). Results The results are shown in table 1 Table 1 Mental-disorder intossication/substance-abuse May1-October 20,2020 May1- October 20,2019 May1-October 20,2020 May1- October 20, 2019 number of ED access 543 564 182 254 higher (yellow and red) priority time to doc (%) 28% 29% 50% 39% worse exit severity codes (%) 10% 6% 16% 11% rate of hospitalization (%) 26% 20% 16% 9% average waiting times (min) 60 64 76 79 LOS lenght of stay (min) 369 326 629 506 access block (%) 3% 2% 5% 4% Total Access Block Time: examination rooms (min) 11.538 8.384 8.059 8.889 Total Access Block Time: holding area (min) 8.382 3.963 182 254 Conclusions We would like to thank all employees of the IRCCS Policlinico San Matteo Foundation for their extraordinary efforts during the pandemic.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".