Core competency in mechanical ventilation
Bibliographic record
Abstract
OBJECTIVES: We sought to identify and standardize the core clinical knowledge and skills required to care for patients receiving mechanical ventilation. DESIGN: Prospective survey reaching consensus by the Delphi technique. SETTING: North American survey conducted anonymously by electronic e-mail. SUBJECTS: International experts in mechanical ventilation, frontline resident educators, medical education experts, and community intensivists were recruited to participate MEASUREMENTS AND MAIN RESULTS: Fourteen panelists participated (ten content experts, three resident educators, one medical education expert, zero community intensivists). Individual panelists generated a total of 200 educational objectives, of which 109 were duplicates. Of the remaining 91 items, 56 met predefined consensus criteria for inclusion in the final set of educational objectives. The educational objectives spanned a broad range of categories, including respiratory physiology, noninvasive ventilation, lung protective ventilation, weaning, and withholding and withdrawing mechanical ventilation. Agreement among panelists on the items included was high (median proportion supporting item inclusion was 88%, range 70%-100%). CONCLUSIONS: There is a consensus that general resident core competency in mechanical ventilation requires a broad range of knowledge application and skill. These educational objectives may help identify and standardize the educational outcomes related to mechanical ventilation that residents should achieve.
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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".