A Questionnaire-Based Survey to Assess the Timing of Intubation in COVID-19 Pneumonia.
Bibliographic record
Abstract
Abstract BackgroundMany COVID19 pneumonia patients progress to Acute respiratory distress syndrome and end up in Intensive Care Units. Given that it is a novel viral infection, the progress of the disease, its management and associated outcomes are yet to be studied in detail. ARDS associated with COVID 19 is the same as before or different and the timing of intubation in such patients is a topic up for debate. This survey aimed to assess the opinion regarding management of COVID 19 ARDS and the timing of intubation in those patients.Methods292 clinicians including anesthesiologists, intensivists and others involved in managing COVID 19 ARDS patients at various centres were surveyed with web-based questionnaire cross sectionally within the time period of 10th June 2020 to 31st August 2020 after taking prior consent. Their responses were recorded and analyzed with statistical software IBM SPSS version 25.0.Results Among 292 included participants, 172 were intensivist, 84 were anesthesiologists and rest were others. Most of the intensivists (51.2%) had seen more than 100 COVID 19 severe ARDS patients. Around 82% of clinicians were agreed that COVID 19 ARDS was different from another form of ARDS. 67.1% of participants were agreed with patient induced self-inflicted injury could have happened in this disease. Likewise, around 91.8% of doctors involved in managing patients were believed that HFNC could be helpful if there were falling of saturation. 37% of participants were not agreed with early intubation, which may increase the risk of mortality and nosocomial infections.Conclusions and RelevanceThere was confusion in most doctors with intubation timing even if there was an indication for intubation. These confusions may be due to non-availability of specific recommendation regarding intubation in COVID 19 severe ARDS patients. However, most of the literature recommended for early intubation in these patients when indicated.
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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 | 0.003 | 0.005 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".