Routine Surveillance Versus Independent Assessment by an Outcome Adjudication Committee in Assessing Patients for Sternal Surgical Site Infections After Cardiac Surgery
Bibliographic record
Abstract
Background. Independent data collection and assessment of possible cases by an outcome adjudication committee (OAC) is considered the gold standard for clinical trials where surgical site infections (SSI) are an outcome. It is unclear, however, whether routine infection control surveillance alone is sufficiently accurate to detect SSIs in clinical trials. Methods. We included all patients undergoing cardiac surgery by sternotomy at two Canadian high-volume centers (Hamilton, ON and Edmonton, AB) over a 4 month period. All patients were assessed through routine infection control surveillance using CDC/NHSN criteria, including chart review 90 days or later after surgery. Charts were also independently reviewed by a research assistant who, blinded to surveillance results, contacted patients 90 days after surgery, and presented patients flagged for a potential sternal SSI (s-SSI) to an OAC (three infectious disease physicians and one cardiac surgeon). The accuracy of surveillance compared with assessment by the OAC in identifying deep/organ space s-SSI using the CDC/NHSN definitions was assessed. Results. A total of 966 patients were included. There were 12 deep infections identified (1.2%) by surveillance and 11 (1.1%) identified by the OAC. There was disagreement in 7 cases (κ = 69.2%). Compared with the OAC, sensitivity and specificity of routine surveillance was 72.7% (8 of 11; 95% confidence interval 39.03-93.98) and 99.6% (951 of 955; 95% confidence interval 98.93-99.89), respectively. The three cases with a deep/organ space s-SSI that were missed by surveillance, were not re-admitted to the study hospital and could therefore only be identified by a follow-up phone call by the research assistant. The four cases that were identified by surveillance but not by the OAC did not meet the CDC/NHSN definition, with 3 of 4 (75%) being treated for presumed deep s-SSI by infectious disease physicians. Conclusion. Routine surveillance alone did not prove to be sufficiently accurate in identifying deep/organ space s-SSIs. Adding a phone call at 90 days to routine surveillance, however, plus an independent review by an OAC of cases identified as deep/organ space infections by routine surveillance, would improve the accuracy and may allow the use of surveillance data as a basis for cardiac surgery clinical trials. Disclosures. All authors: No reported disclosures.
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 | 0.137 | 0.228 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".