Is There a Surgeons’ Effect on Patients’ Physical Health, Beyond the Intervention, That Requires Further Investigation? A Systematic Review
Bibliographic record
Abstract
Christoph Schnelle,1 Justin Clark,1 Rachel Mascord,2 Mark A Jones1 1Institute for Evidence-Based Healthcare, Bond University, Robina, Queensland, Australia; 2General Dentist, BMA House, Sydney, New South Wales, AustraliaCorrespondence: Christoph Schnelle, Institute for Evidence-Based Healthcare, Bond University, Robina, Queensland, Australia, Email christoph.schnelle@student.bond.edu.auObjective: To find and review published papers researching surgeons’ effects on patients’ physical health. Clinical outcomes of surgery patients with similar prognoses cannot be fully explained by surgeon skill or experience. Just as there are “hospital” and “psychotherapist” effects, there may be “surgeons” effects that persist after controlling for known variables like patient health and operation riskiness.Methods: Cohort studies and randomized controlled trials (RCTs) of any surgical intervention, which, after multivariate adjustment, either showed proportion of variance in patients’ physical health outcomes due to surgeons (random effects) or graded surgeons from best to worst (fixed effects). Studies with < 15 surgeons or only ascribing surgeons’ effects to known variables excluded. Medline, PubMed, Embase, and PsycINFO were used for search until June 2020. Manual search for papers referring/referred by resulting studies. Risk of bias assessed by Cochrane risk-of-bias tool and Newcastle–Ottawa Scale.Results: Included studies: 52 cohort studies and three RCTs of 52,436+ surgeons covering 102 outcomes (33 unique). Studies either graded surgeons from best to worst or calculated the intra-class correlation coefficient (ICC), the percentage of patients’ variation due to surgeons, in diverse ways. Sixteen studies showed exceptionally good and/or bad performers with confidence intervals wholly above or below the average performance. ICCs ranged from 0 to 47%, median 4.0%. There are no well-established reporting standards; highly heterogeneous reporting, therefore no meta-analysis.Discussion: Interpretation: There is a surgeons’ effect on patients’ physical health for many types of surgeries and outcomes, ranging from small to substantial. Surgeons with exceptional patient outcomes appear regularly even after accounting for all known confounding variables. Many existing cohort studies and RCTs could be reanalyzed for surgeons’ effects especially after methodological reporting guidelines are published.Conclusion: In terms of patient outcomes, it can matter which surgeon is chosen. Surgeons with exceptional patient outcomes are worth studying further.Keywords: physicians, physicians’ effect, doctors’ effect, therapists’ effect, practice effect, clinical competence, professional practice gap, surgeons’ practice pattern, quality of health care, delivery of health care
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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 | 0.046 | 0.189 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.015 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".