Annals of Internal Medicine最新文献

Clinical impact ratings: GIM/FP/GP: [Formula: see text] Phys Med & Rehab: [Formula: see text].

Background: In embarking on randomized clinical trials (RCTs), researchers can hypothesize that a more intensive treatment is better than a less intensive treatment (positive hypothesis) or that a less intensive treatment is similar or noninferior to a more intensive treatment (negative hypothesis). Researchers may design noninferiority RCTs (NI-RCTs) to support negative hypotheses and standard RCTs (S-RCTs) to support negative or positive hypotheses. Regardless of hypotheses, S-RCTs and NI-RCTs should produce consistent results when assessing similar participants, interventions, control, and outcomes.

Objective: To compare effect estimates in S-RCTs with positive hypotheses versus NI-RCTs and in S-RCTs with negative hypotheses versus NI-RCTs.

Design: Meta-research.

Setting: 98 meta-analyses.

Participants: 468 RCTs, including 153 NI-RCTs and 315 S-RCTs (149 positive and 166 negative hypotheses).

Intervention: S-RCTs as the exposure and NI-RCTs as the control.

Measurements: The ratio of effect estimates between S-RCTs and NI-RCTs in each meta-analysis was combined across meta-analyses.

Results: Standard RCTs with positive hypotheses produced effect estimates 1.47 (95% CI, 1.27 to 1.70) times larger than NI-RCTs; among RCTs rated as having low risk of bias for blinding, the ratio was 1.01 (CI, 0.70 to 1.45), whereas among those rated as having high or unclear risk of bias for blinding, the ratio was 1.81 (CI, 1.41 to 2.33). Standard RCTs with negative hypotheses did not produce statistically different effect estimates from NI-RCTs (ratio, 0.93 [CI, 0.84 to 1.03]).

Limitation: Findings may be limited by residual differences between S-RCTs and NI-RCTs in the same meta-analysis.

Conclusion: The researchers' hypotheses may bias the results of published RCTs, especially those with high or unclear risk of bias for blinding. The effect of researchers' hypotheses should be assessed in systematic reviews and clinical practice guidelines when RCTs addressing the same clinical question report conflicting hypotheses.

Primary funding source: The Shenzhen Municipal Government, Guangdong Province, China, and the Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences.

Background: Hospital-based violence intervention programs (HVIPs) are widespread, but their effectiveness for violence prevention is unclear.

Objective: To determine the effects of Boston Medical Center's HVIP on future violence outcomes among violently injured young adults.

Design: Target trial emulation using observational data.

Setting: Boston, Massachusetts.

Participants: Young adults aged 16 to 34 years who survived a shooting or stabbing.

Intervention: Target trials of 2 treatment strategies using the same eligibility criteria, time zero, and control group were emulated: 1) any treatment: engaging with the HVIP within 1 month of injury and 2) sustained treatment: initiating within 1 month and engaging more than 4 of the first 8 weeks.

Measurements: Combined measure of violent reinjury or violence perpetration at 1, 2, and 3 years, using hospital and police data.

Results: 1328 patients met criteria; 565 (42.5%) initiated within 1 month. Of these, 58 (10.2%) sustained engagement. In the any-treatment analysis, estimated cumulative incidence was roughly equal between the treatment and control strategies at 1, 2, and 3 years. In the sustained engagement analysis, treatment was associated with considerably lower cumulative incidence (4.5% [95% CI, 1.1% to 9.3%] at 1 year; 5.1% [CI, 1.1% to 9.3%] at 2 years; 6.4% [CI, 1.4% to 12.9%] at 3 years) versus the control strategy (8.7% [6.6% to 10.0%] at 1 year; 12.3% [10.2% to 14.5%] at 2 years; 14.3% [11.8% to 16.6%] at 3 years), with corresponding risk reductions of 47.6% (-19.8% to 86.7%), 58.5% (21.6% to 91.2%), and 55.3% (4.9% to 90.2%). Confidence intervals were wide.

Limitation: Despite our target trial emulation approach, results could be confounded by unmeasured factors associated with program engagement.

Conclusion: Although HVIPs can improve long-term violence outcomes, these effects seem to require intensive participant engagement.

Primary funding source: Fund for a Safer Future.

Real-time federal surveillance of diseases and health care delivery informs clinical guidance and public health policy. However, in 2025, some U.S. Centers for Disease Control and Prevention (CDC) databases seemed to have "unexplained pauses" and ceased or delayed updates. The CDC public data catalog was audited to identify paused databases that had previously been updated at least monthly and evaluated their characteristics. Of 1359 catalog records examined on 28 October 2025, eighty-two were previously updated at least monthly. On the basis of each database's stated periodicity, allowing for an additional 30-day grace period, their status was classified as either current or paused as of 28 October 2025. Forty-four databases (54%) were current, and 38 (46%) were paused. Thirty-four of the 38 databases (89%) had no data entries dated within 6 months of the date of analysis, whereas 4 (11%) paused more recently. Of the 38 paused databases, 33 (87%) were vaccination-related topics compared with none of the 44 current databases. Of the 5 paused databases on other topics, 4 addressed respiratory diseases, including disease burden and nonvaccine prevention measures, whereas 1 addressed public health (drug overdose deaths). The persistence of pauses as of 2 December 2025 was examined. Only 1 of the 38 paused databases had been updated. Such long pauses may have compromised evidence for decision making and policies by clinicians, administrators, professional organizations, and policymakers. Federal databases should adopt minimum transparency standards, including displaying the current update status, with a rationale if paused, and next expected update with criteria for resumption. Without such standards, unexplained pauses in surveillance risk undermining evidence-based medicine and public trust.

Background: Slow correction of severe hyponatremia is recommended to prevent osmotic demyelination syndrome but is associated with higher mortality.

Objective: To examine the association between sodium correction rates and death or delayed neurologic events.

Design: Retrospective cohort study.

Setting: Twenty-one community hospitals of an integrated health system in northern California.

Patients: Adults hospitalized with a serum sodium level of 120 mEq/L or lower between 2008 and 2023.

Intervention: Maximum 24-hour rate of serum sodium correction (slow [<8 mEq/L], medium [8 to 12 mEq/L], or fast [>12 mEq/L; reference]).

Measurements: The primary outcome was a composite of 90-day death or delayed neurologic events (new demyelination, paralysis, epilepsy, or altered consciousness between 3 and 90 days from admission). Standardized risk differences (RDs) were generated using targeted maximum likelihood estimation. Heterogeneity of effect was assessed across grades of predicted risk.

Results: 13 988 patients were hospitalized with severe hyponatremia during the study period (median age, 74 years; 63% female). Comorbidities included congestive heart failure (24%), liver disease (18%), alcohol dependence (14%), and metastatic cancer (10%). The primary outcome occurred in 3000 patients (21%); 90-day death occurred in 2554 (18%), and 90-day delayed neurologic events occurred in 587 (4%). Compared with slow 24-hour sodium correction, both medium (RD, -5.6 percentage points [95% CI, -7.1 to -4.0 percentage points]) and fast (RD, -9.0 percentage points [CI, -11.1 to -6.9 percentage points]) correction rates were associated with lower adjusted risk for the primary outcome. Risk differences increased with higher predicted risk, whereas risk ratios remained similar.

Limitations: Residual confounding; outcome ascertainment using diagnostic codes.

Conclusion: Faster sodium correction is associated with lower risk for 90-day death or delayed neurologic events. Treatment guidelines should be reexamined.

Primary funding source: The Permanente Medical Group Rapid Analytics Unit Program.