Is There a Doctors’ Effect on Patients’ Physical Health, Beyond the Intervention and All Known Factors? A Systematic Review
Bibliographic record
Abstract
Christoph Schnelle,1 Justin Clark,1 Rachel Mascord,2 Mark A Jones1 1Institute of Evidence-Based Healthcare, Bond University, Robina, Queensland, Australia; 2General Dentist, BMA House, Sydney, New South Wales, AustraliaCorrespondence: Christoph Schnelle, Institute of Evidence-Based Healthcare, Bond University, Robina, Queensland, Australia, Email christoph.schnelle@student.bond.edu.auPurpose: Despite billions of doctor visits worldwide each year, little is known on whether doctors themselves affect patients’ physical health after accounting for intervention and confounders such as patients’ and doctors’ data, hospital effects, nor how strong that doctors’ effect is. Knowledge of surgeons’ and psychotherapists’ effects exists, but not for 102 other medical specialties notwithstanding the importance of such knowledge.Methods: Eligibility Criteria: Randomized controlled trials (RCTs), case-control, and cohort studies including medical doctors except surgeons for any intervention, reporting the proportion of variance in patients’ outcomes owing to the doctors (random effects), or the fixed effects of grading doctors by outcomes, after multivariate adjustment. Exclusions: studies of < 15 doctors or solely reporting doctors’ effects for known variables.Sources: Medline, Embase, PsycINFO, inception to June 2020. Manual search for papers referring/referred to by resulting studies.Risk of Bias: Using Newcastle–Ottawa scale.Results: Despite all medical interventions bar surgery being eligible, only thirty cohort papers were found, covering 36,239 doctors, with 10 specialties, 21 interventions, 60 outcomes (17 unique). Studies reported doctors’ effects by grading doctors from best to worst, or by diversely calculating the doctor-attributed percentage of patients’ outcome variation, ie the intra-class correlation coefficient (ICC). Sixteen studies presented fixed effects, 18 random effects, and 3 another approach. No RCTs found. Thirteen studies reported exceptionally good and/or poor performers with confidence intervals wholly outside the average performance. ICC range 0 to 33%, mean 3.9%. Highly diverse reporting, meta-analysis therefore not applicable.Conclusion: Doctors, on their own, can affect patients’ physical health for many interventions and outcomes. Effects range from negligible to substantial, even after accounting for all known variables. Many published cohorts may reveal valuable information by reanalyzing their data for doctors’ effects. Positive and negative doctor outliers appear regularly. Therefore, it can matter which doctor is chosen.Keywords: physicians’ effect, practice effect, physicians’ practice pattern, clinical competence, professional practice gap, delivery of health care, quality of health care, physicians
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 | 0.055 | 0.197 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.015 |
| Bibliometrics | 0.005 | 0.006 |
| 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) | 0.007 | 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".